Vehicle rider satisfaction promoting systems based on adjusting operating parameters of the vehicle

ABSTRACT

Data processing systems disclosed herein may promote satisfaction of a rider of a vehicle and include a machine learning model and a vehicle control system. The machine learning model determines a measure of an emotional state of the rider based on data received from a sensor associated with the rider, where the data is indicative of a physiological condition of the rider. The vehicle control system: determines a target value of an operating parameter of the vehicle based on a correlation between the emotional state of the rider and the target value of the operating parameter; and adjusts the operating parameter of the vehicle based on the target value of the operating parameter.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/803,356, filed Feb. 27, 2020, which itself is a continuation ofInternational Application S.N. PCT/US2019/053857, filed Sep. 30, 2019,which itself claims priority to U.S. provisional application No.62/739,335, filed Sep. 30, 2018, each of which is hereby incorporated byreference as if fully set forth herein in its entirety.

TECHNICAL FIELD

The present disclosure relates to intelligent transportation systems,and in examples, more particularly relates to inter-connectivity andoptimization of user experiences in transportation systems.

BACKGROUND

As artificial intelligence, cognitive networking, sensor technologies,storage technologies (e.g., blockchain and other distributed ledgertechnologies) and other technologies progress, opportunities exist fordevelopment of systems that enable improved mobility and transportationfor passengers and for objects, such as freight, goods, animals and thelike. A need exists for improved transportation systems that takeadvantage of such technologies and their capabilities.

Some applications of artificial intelligence have been, at least to adegree, effective at accomplishing certain tasks, such as tasksinvolving recognition and classification of objects and behavior, suchas in natural language processing (NLP) and computer vision systems.However, in complex, dynamic systems that involve interactions ofelements, such as transportation systems that involve sets of complexchemical processes (e.g., involving combustion processes, heating andcooling, battery charging and discharging), mechanical systems, andhuman systems (involving individual and group behaviors), significantchallenges exist in classifying, predicting and optimizing system-levelinteractions and behaviors. A need exists for systems apply specializedcapabilities of different types of neural networks and other artificialintelligence technologies and for systems that enable selectivedeployment of such technologies, as well as various hybrids andcombinations of such technologies.

SUMMARY

Among other things, provided herein are methods, systems, components,processes, modules, blocks, circuits, sub-systems, articles, and otherelements (collectively referred to in some cases as the “platform” orthe “system,” which terms should be understood to encompass any of theabove except where context indicates otherwise) that individually orcollectively enable advances in transportation systems.

An aspect provided herein includes a rider state modification system forimproving a state of a rider in a vehicle, the system comprising: afirst neural network that operates to classify a state of the vehiclethrough analysis of information about the vehicle captured by anInternet-of-things device during operation of the vehicle; and a secondneural network that operates to optimize at least one operatingparameter of the vehicle based on the classified state of the vehicle,information about a state of a rider occupying the vehicle, andinformation that correlates vehicle operation with an effect on riderstate.

In embodiments, the vehicle comprises a system for automating at leastone control parameter of the vehicle. In embodiments, the vehicle is atleast a semi-autonomous vehicle. In embodiments, the vehicle isautomatically routed. In embodiments, the vehicle is a self-drivingvehicle. In embodiments, the at least one Internet-of-things device isdisposed in an operating environment of the vehicle. In embodiments, theat least one Internet-of-things device that captures the data about thevehicle is disposed external to the vehicle. In embodiments, the atleast one Internet-of-things device is a dashboard camera. Inembodiments, the at least one Internet-of-things device is a mirrorcamera. In embodiments, the at least one Internet-of-things device is amotion sensor. In embodiments, the at least one Internet-of-thingsdevice is a seat-based sensor system. In embodiments, the at least oneInternet-of-things device is an IoT enabled lighting system.

In embodiments, the lighting system is a vehicle interior lightingsystem. In embodiments, the lighting system is a headlight lightingsystem. In embodiments, the at least one Internet-of-things device is atraffic light camera or sensor. In embodiments, the at least oneInternet-of-things device is a roadway camera. In embodiments, theroadway camera is disposed on at least one of a telephone phone and alight pole. In embodiments, the at least one Internet-of-things deviceis an in-road sensor. In embodiments, the at least oneInternet-of-things device is an in-vehicle thermostat. In embodiments,the at least one Internet-of-things device is a toll booth. Inembodiments, the at least one Internet-of-things device is a streetsign. In embodiments, the at least one Internet-of-things device is atraffic control light. In embodiments, the at least oneInternet-of-things device is a vehicle mounted sensor. In embodiments,the at least one Internet-of-things device is a refueling system. Inembodiments, the at least one Internet-of-things device is a rechargingsystem. In embodiments, the at least one Internet-of-things device is awireless charging station.

It is to be understood that any combination of features from the methodsdisclosed herein and/or from the systems disclosed herein may be usedtogether, and/or that any features from any or all of these aspects maybe combined with any of the features of the embodiments and/or examplesdisclosed herein to achieve the benefits as described in thisdisclosure.

BRIEF DESCRIPTION OF THE FIGURES

In the accompanying figures, like reference numerals refer to identicalor functionally similar elements throughout the separate views andtogether with the detailed description below are incorporated in andform part of the specification, serve to further illustrate variousembodiments and to explain various principles and advantages all inaccordance with the systems and methods disclosed herein.

FIG. 1 is a diagrammatic view that illustrates an architecture for atransportation system showing certain illustrative components andarrangements relating to various embodiments of the present disclosure.

FIG. 2 is a diagrammatic view that illustrates use of a hybrid neuralnetwork to optimize a powertrain component of a vehicle relating tovarious embodiments of the present disclosure.

FIG. 3 is a diagrammatic view that illustrates a set of states that maybe provided as inputs to and/or be governed by an expertsystem/Artificial Intelligence (AI) system relating to variousembodiments of the present disclosure.

FIG. 4 is a diagrammatic view that illustrates a range of parametersthat may be taken as inputs by an expert system or AI system, orcomponent thereof, as described throughout this disclosure, or that maybe provided as outputs from such a system and/or one or more sensors,cameras, or external systems relating to various embodiments of thepresent disclosure.

FIG. 5 is a diagrammatic view that illustrates a set of vehicle userinterfaces relating to various embodiments of the present disclosure.

FIG. 6 is a diagrammatic view that illustrates a set of interfaces amongtransportation system components relating to various embodiments of thepresent disclosure.

FIG. 7 is a diagrammatic view that illustrates a data processing system,which may process data from various sources relating to variousembodiments of the present disclosure.

FIG. 8 is a diagrammatic view that illustrates a set of algorithms thatmay be executed in connection with one or more of the many embodimentsof transportation systems described throughout this disclosure relatingto various embodiments of the present disclosure.

FIG. 9 is a diagrammatic view that illustrates systems describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 10 is a diagrammatic view that illustrates systems describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 11 is a diagrammatic view that illustrates a method describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 12 is a diagrammatic view that illustrates systems describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 13 is a diagrammatic view that illustrates a method describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 14 is a diagrammatic view that illustrates systems describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 15 is a diagrammatic view that illustrates a method describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 16 is a diagrammatic view that illustrates systems describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 17 is a diagrammatic view that illustrates a method describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 18 is a diagrammatic view that illustrates systems describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 19 is a diagrammatic view that illustrates a method describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 20 is a diagrammatic view that illustrates a method describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 21 is a diagrammatic view that illustrates a method describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 22 is a diagrammatic view that illustrates systems describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 23 is a diagrammatic view that illustrates a method describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 24 is a diagrammatic view that illustrates a method describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 25 is a diagrammatic view that illustrates systems describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 26 is a diagrammatic view that illustrates a method describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 26A is a diagrammatic view that illustrates systems describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 27 is a diagrammatic view that illustrates systems describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 28 is a diagrammatic view that illustrates a method describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 29 is a diagrammatic view that illustrates systems describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 30 is a diagrammatic view that illustrates systems describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 31 is a diagrammatic view that illustrates systems describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 32 is a diagrammatic view that illustrates systems describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 33 is a diagrammatic view that illustrates a method describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 34 is a diagrammatic view that illustrates systems describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 35 is a diagrammatic view that illustrates a method describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 36 is a diagrammatic view that illustrates systems describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 37 is a diagrammatic view that illustrates systems describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 38 is a diagrammatic view that illustrates a method describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 39 is a diagrammatic view that illustrates a method describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 40 is a diagrammatic view that illustrates a method describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 41 is a diagrammatic view that illustrates systems describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 42 is a diagrammatic view that illustrates a method describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 43 is a diagrammatic view that illustrates a method describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 44 is a diagrammatic view that illustrates systems describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 45 is a diagrammatic view that illustrates systems and methodsdescribed throughout this disclosure relating to various embodiments ofthe present disclosure.

FIG. 46 is a diagrammatic view that illustrates systems and methodsdescribed throughout this disclosure relating to various embodiments ofthe present disclosure.

FIG. 47 is a diagrammatic view that illustrates systems and methodsdescribed throughout this disclosure relating to various embodiments ofthe present disclosure.

FIG. 48 is a diagrammatic view that illustrates systems describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 49 is a diagrammatic view that illustrates a method describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 50 is a diagrammatic view that illustrates a method describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 51 is a diagrammatic view that illustrates systems describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 52 is a diagrammatic view that illustrates systems describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 53 is a diagrammatic view that illustrates systems describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 54 is a diagrammatic view that illustrates a method describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 55 is a diagrammatic view that illustrates a method describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 56 is a diagrammatic view that illustrates systems describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 57 is a diagrammatic view that illustrates systems describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

FIG. 58 is a diagrammatic view that illustrates systems describedthroughout this disclosure relating to various embodiments of thepresent disclosure.

Skilled artisans will appreciate that elements in the figures areillustrated for simplicity and clarity and have not necessarily beendrawn to scale. For example, the dimensions of some of the elements inthe figures may be exaggerated relative to other elements to help toimprove understanding of the many embodiments of the systems and methodsdisclosed herein.

DETAILED DESCRIPTION

The present disclosure will now be described in detail by describingvarious illustrative, non-limiting embodiments thereof with reference tothe accompanying drawings and exhibits. The disclosure may, however, beembodied in many different forms and should not be construed as beinglimited to the illustrative embodiments set forth herein. Rather, theembodiments are provided so that this disclosure will be thorough andwill fully convey the concept of the disclosure to those skilled in theart. The claims should be consulted to ascertain the true scope of thedisclosure.

Before describing in detail embodiments that are in accordance with thesystems and methods disclosed herein, it should be observed that theembodiments reside primarily in combinations of method and/or systemcomponents. Accordingly, the system components and methods have beenrepresented where appropriate by conventional symbols in the drawings,showing only those specific details that are pertinent to understandingthe embodiments of the systems and methods disclosed herein.

All documents mentioned herein are hereby incorporated by reference intheir entirety. References to items in the singular should be understoodto include items in the plural, and vice versa, unless explicitly statedotherwise or clear from the context. Grammatical conjunctions areintended to express any and all disjunctive and conjunctive combinationsof conjoined clauses, sentences, words, and the like, unless otherwisestated or clear from the context. Thus, the term “or” should generallybe understood to mean “and/or” and so forth, except where the contextclearly indicates otherwise.

Recitation of ranges of values herein are not intended to be limiting,referring instead individually to any and all values falling within therange, unless otherwise indicated herein, and each separate value withinsuch a range is incorporated into the specification as if it wereindividually recited herein. The words “about,” “approximately,” or thelike, when accompanying a numerical value, are to be construed asindicating a deviation as would be appreciated by one skilled in the artto operate satisfactorily for an intended purpose. Ranges of valuesand/or numeric values are provided herein as examples only, and do notconstitute a limitation on the scope of the described embodiments. Theuse of any and all examples, or exemplary language (“e.g.,” “such as,”or the like) provided herein, is intended merely to better illuminatethe embodiments and does not pose a limitation on the scope of theembodiments or the claims. No language in the specification should beconstrued as indicating any unclaimed element as essential to thepractice of the embodiments.

In the following description, it is understood that terms such as“first,” “second,” “third,” “above,” “below,” and the like, are words ofconvenience and are not to be construed as implying a chronologicalorder or otherwise limiting any corresponding element unless expresslystated otherwise. The term “set” should be understood to encompass a setwith a single member or a plurality of members.

Referring to FIG. 1 , an architecture for a transportation system 111 isdepicted, showing certain illustrative components and arrangementsrelating to certain embodiments described herein. The transportationsystem 111 may include one or more vehicles 110, which may includevarious mechanical, electrical, and software components and systems,such as a powertrain 113, a suspension system 117, a steering system, abraking system, a fuel system, a charging system, seats 128, acombustion engine, an electric vehicle drive train, a transmission 119,a gear set, and the like. The vehicle may have a vehicle user interface123, which may include a set of interfaces that include a steeringsystem, buttons, levers, touch screen interfaces, audio interfaces, andthe like as described throughout this disclosure. The vehicle may have aset of sensors 125 (including cameras 127), such as for providing inputto expert system/artificial intelligence features described throughoutthis disclosure, such as one or more neural networks (which may includehybrid neural networks 147 as described herein). Sensors 125 and/orexternal information may be used to inform the expert system/ArtificialIntelligence (AI) system 136 and to indicate or track one or morevehicle states 144, such as vehicle operating states 345 (FIG. 3 ), userexperience states 346 (FIG. 3 ), and others described herein, which alsomay be as inputs to or taken as outputs from a set of expert system/AIcomponents. Routing information 143 may inform and take input from theexpert system/AI system 136, including using in-vehicle navigationcapabilities and external navigation capabilities, such as GlobalPosition System (GPS), routing by triangulation (such as cell towers),peer-to-peer routing with other vehicles 121, and the like. Acollaboration engine 129 may facilitate collaboration among vehiclesand/or among users of vehicles, such as for managing collectiveexperiences, managing fleets and the like. Vehicles 110 may be networkedamong each other in a peer-to-peer manner, such as using cognitiveradio, cellular, wireless or other networking features. An AI system 136or other expert systems may take as input a wide range of vehicleparameters 130, such as from on board diagnostic systems, telemetrysystems, and other software systems, as well as from vehicle-locatedsensors 125 and from external systems. In embodiments, the system maymanage a set of feedback/rewards 148, incentives, or the like, such asto induce certain user behavior and/or to provide feedback to the AIsystem 136, such as for learning on a set of outcomes to accomplish agiven task or objective. The expert system or AI system 136 may inform,use, manage, or take output from a set of algorithms 149, including awide variety as described herein. In the example of the presentdisclosure depicted in FIG. 1 , a data processing system 162, isconnected to the hybrid neural network 147. The data processing system162 may process data from various sources (see FIG. 7 ). In the exampleof the present disclosure depicted in FIG. 1 , a system user interface163, is connected to the hybrid neural network 147. See the disclosure,below, relating to FIG. 6 for further disclosure relating to interfaces.FIG. 1 shows that vehicle surroundings 164 may be part of thetransportation system 111. Vehicle surroundings may include roadways,weather conditions, lighting conditions, etc. FIG. 1 shows that devices165, for example, mobile phones and computer systems, navigationsystems, etc., may be connected to various elements of thetransportation system 111, and therefore may be part of thetransportation system 111 of the present disclosure.

Referring to FIG. 2 , provided herein are transportation systems havinga hybrid neural network 247 for optimizing a powertrain 213 of avehicle, wherein at least two parts of the hybrid neural network 247optimize distinct parts of the powertrain 213. An artificialintelligence system may control a powertrain component 215 based on anoperational model (such as a physics model, an electrodynamic model, ahydrodynamic model, a chemical model, or the like for energy conversion,as well as a mechanical model for operation of various dynamicallyinteracting system components). For example, the AI system may control apowertrain component 215 by manipulating a powertrain operatingparameter 260 to achieve a powertrain state 261. The AI system may betrained to operate a powertrain component 215, such as by training on adata set of outcomes (e.g., fuel efficiency, safety, rider satisfaction,or the like) and/or by training on a data set of operator actions (e.g.,driver actions sensed by a sensor set, camera or the like or by avehicle information system). In embodiments, a hybrid approach may beused, where one neural network optimizes one part of a powertrain (e.g.,for gear shifting operations), while another neural network optimizesanother part (e.g., braking, clutch engagement, or energy discharge andrecharging, among others). Any of the powertrain components describedthroughout this disclosure may be controlled by a set of controlinstructions that consist of output from at least one component of ahybrid neural network 247.

FIG. 3 illustrates a set of states that may be provided as inputs toand/or be governed by an expert system/AI system 336, as well as used inconnection with various systems and components in various embodimentsdescribed herein. States 344 may include vehicle operating states 345,including vehicle configuration states, component states, diagnosticstates, performance states, location states, maintenance states, andmany others, as well as user experience states 346, such asexperience-specific states, emotional states 366 for users, satisfactionstates 367, location states, content/entertainment states and manyothers.

FIG. 4 illustrates a range of parameters 430 that may be taken as inputsby an expert system or AI system 136 (FIG. 1 ), or component thereof, asdescribed throughout this disclosure, or that may be provided as outputsfrom such a system and/or one or more sensors 125 (FIG. 1 ), cameras 127(FIG. 1 ), or external systems. Parameters 430 may include one or moregoals 431 or objectives (such as ones that are to be optimized by anexpert system/AI system, such as by iteration and/or machine learning),such as a performance goal 433, such as relating to fuel efficiency,trip time, satisfaction, financial efficiency, safety, or the like.Parameters 430 may include market feedback parameters 435, such asrelating to pricing, availability, location, or the like of goods,services, fuel, electricity, advertising, content, or the like.Parameters 430 may include rider state parameters 437, such asparameters relating to comfort 439, emotional state, satisfaction,goals, type of trip, fatigue and the like. Parameters 430 may includeparameters of various transportation-relevant profiles, such as trafficprofiles 440 (location, direction, density and patterns in time, amongmany others), road profiles 441 (elevation, curvature, direction, roadsurface conditions and many others), user profiles, and many others.Parameters 430 may include routing parameters 442, such as currentvehicle locations, destinations, waypoints, points of interest, type oftrip, goal for trip, required arrival time, desired user experience, andmany others. Parameters 430 may include satisfaction parameters 443,such as for riders (including drivers), fleet managers, advertisers,merchants, owners, operators, insurers, regulators and others.Parameters 430 may include operating parameters 444, including the widevariety described throughout this disclosure.

FIG. 5 illustrates a set of vehicle user interfaces 523. Vehicle userinterfaces 523 may include electromechanical interfaces 568, such assteering interfaces, braking interfaces, interfaces for seats, windows,moonroof, glove box and the like. Interfaces 523 may include varioussoftware interfaces (which may have touch screen, dials, knobs, buttons,icons or other features), such as a game interface 569, a navigationinterface 570, an entertainment interface 571, a vehicle settingsinterface 572, a search interface 573, an ecommerce interface 574, andmany others. Vehicle interfaces may be used to provide inputs to, andmay be governed by, one or more AI systems/expert systems such asdescribed in embodiments throughout this disclosure.

FIG. 6 illustrates a set of interfaces among transportation systemcomponents, including interfaces within a host system (such as governinga vehicle or fleet of vehicles) and host interfaces 650 between a hostsystem and one or more third parties and/or external systems. Interfacesinclude third party interfaces 655 and end user interfaces 651 for usersof the host system, including the in-vehicle interfaces that may be usedby riders as noted in connection with FIG. 5 , as well as userinterfaces for others, such as fleet managers, insurers, regulators,police, advertisers, merchants, content providers, and many others.Interfaces may include merchant interfaces 652, such as by whichmerchants may provide advertisements, content relating to offerings, andone or more rewards, such as to induce routing or other behavior on thepart of users. Interfaces may include machine interfaces 653, such asapplication programming interfaces (API) 654, networking interfaces,peer-to-peer interfaces, connectors, brokers, extract-transform-load(ETL) system, bridges, gateways, ports and the like. Interfaces mayinclude one or more host interfaces by which a host may manage and/orconfigure one or more of the many embodiments described herein, such asconfiguring neural network components, setting weight for models,setting one or more goals or objectives, setting reward parameters 656,and many others. Interfaces may include expert system/AI systemconfiguration interfaces 657, such as for selecting one or more models658, selecting and configuring data sets 659 (such as sensor data,external data and other inputs described herein), AI selection 660 andAI configuration 661 (such as selection of neural network category,parameter weighting and the like), feedback selection 662 for an expertsystem/AI system, such as for learning, and supervision configuration663, among many others.

FIG. 7 illustrates a data processing system 758, which may process datafrom various sources, including social media data sources 769, weatherdata sources 770, road profile sources 771, traffic data sources 772,media data sources 773, sensors sets 774, and many others. The dataprocessing system may be configured to extract data, transform data to asuitable format (such as for use by an interface system, an AIsystem/expert system, or other systems), load it to an appropriatelocation, normalize data, cleanse data, deduplicate data, store data(such as to enable queries) and perform a wide range of processing tasksas described throughout this disclosure.

FIG. 8 illustrates a set of algorithms 849 that may be executed inconnection with one or more of the many embodiments of transportationsystems described throughout this disclosure. Algorithms 849 may takeinput from, provide output to, and be managed by a set of AIsystems/expert systems, such as of the many types described herein.Algorithms 849 may include algorithms for providing or managing usersatisfaction 874, one or more genetic algorithms 875, such as forseeking favorable states, parameters, or combinations ofstates/parameters in connection with optimization of one or more of thesystems described herein. Algorithms 849 may include vehicle routingalgorithms 876, including ones that are sensitive to various vehicleoperating parameters, user experience parameters, or other states,parameters, profiles, or the like described herein, as well as tovarious goals or objectives. Algorithms 849 may include object detectionalgorithms 876. Algorithms 849 may include energy calculation algorithms877, such as for calculating energy parameters, for optimizing fuelusage, electricity usage or the like, for optimizing refueling orrecharging time, location, amount or the like. Algorithms may includeprediction algorithms, such as for a traffic prediction algorithm 879, atransportation prediction algorithm 880, and algorithms for predictingother states or parameters of transportation systems as describedthroughout this disclosure.

In various embodiments, transportation systems 111 as described hereinmay include vehicles (including fleets and other sets of vehicles), aswell as various infrastructure systems. Infrastructure systems mayinclude Internet of Things systems (such as using cameras and othersensors, such as disposed on or in roadways, on or in traffic lights,utility poles, toll booths, signs and other roadside devices andsystems, on or in buildings, and the like), refueling and rechargingsystems (such as at service stations, charging locations and the like,and including wireless recharging systems that use wireless powertransfer), and many others.

Vehicle electrical, mechanical and/or powertrain components as describedherein may include a wide range of systems, including transmission, gearsystem, clutch system, braking system, fuel system, lubrication system,steering system, suspension system, lighting system (including emergencylighting as well as interior and exterior lights), electrical system,and various subsystems and components thereof.

Vehicle operating states and parameters may include route, purpose oftrip, geolocation, orientation, vehicle range, powertrain parameters,current gear, speed/acceleration, suspension profile (including variousparameters, such as for each wheel), charge state for electric andhybrid vehicles, fuel state for fueled vehicles, and many others asdescribed throughout this disclosure.

Rider and/or user experience states and parameters as describedthroughout this disclosure may include emotional states, comfort states,psychological states (e.g., anxiety, nervousness, relaxation or thelike), awake/asleep states, and/or states related to satisfaction,alertness, health, wellness, one or more goals or objectives, and manyothers. User experience parameters as described herein may furtherinclude ones related to driving, braking, curve approach, seatpositioning, window state, ventilation system, climate control,temperature, humidity, sound level, entertainment content type (e.g.,news, music, sports, comedy, or the like), route selection (such as forPOIs, scenic views, new sites and the like), and many others.

In embodiments, a route may be ascribed various parameters of value,such as parameters of value that may be optimized to improve userexperience or other factors, such as under control of an AIsystem/expert system. Parameters of value of a route may include speed,duration, on time arrival, length (e.g., in miles), goals (e.g., to seea Point of Interest (POI), to complete a task (e.g., complete a shoppinglist, complete a delivery schedule, complete a meeting, or the like),refueling or recharging parameters, game-based goals, and others. As oneof many examples, a route may be attributed value, such as in a modeland/or as an input or feedback to an AI system or expert system that isconfigured to optimize a route, for task completion. A user may, forexample, indicate a goal to meet up with at least one of a set offriends during a weekend, such as by interacting with a user interfaceor menu that allows setting of objectives. A route may be configured(including with inputs that provide awareness of friend locations, suchas by interacting with systems that include location information forother vehicles and/or awareness of social relationships, such as throughsocial data feeds) to increase the likelihood of meeting up, such as byintersecting with predicted locations of friends (which may be predictedby a neural network or other AI system/expert system as describedthroughout this disclosure) and by providing in-vehicle messages (ormessages to a mobile device) that indicates possible opportunities formeeting up.

Market feedback factors may be used to optimize various elements oftransportation systems as described throughout this disclosure, such ascurrent and predicted pricing and/or cost (e.g., of fuel, electricityand the like, as well as of goods, services, content and the like thatmay be available along the route and/or in a vehicle), current andpredicted capacity, supply and/or demand for one or more transportationrelated factors (such as fuel, electricity, charging capacity,maintenance, service, replacement parts, new or used vehicles, capacityto provide ride sharing, self-driving vehicle capacity or availability,and the like), and many others.

An interface in or on a vehicle may include a negotiation system, suchas a bidding system, a price-negotiating system, a reward-negotiatingsystem, or the like. For example, a user may negotiate for a higherreward in exchange for agreeing to re-route to a merchant location, auser may name a price the user is willing to pay for fuel (which may beprovided to nearby refueling stations that may offer to meet the price),or the like. Outputs from negotiation (such as agreed prices, trips andthe like) may automatically result in reconfiguration of a route, suchas one governed by an AI system/expert system.

Rewards, such as provided by a merchant or a host, among others, asdescribed herein may include one or more coupons, such as redeemable ata location, provision of higher priority (such as in collective routingof multiple vehicles), permission to use a “Fast Lane,” priority forcharging or refueling capacity, among many others. Actions that can leadto rewards in a vehicle may include playing a game, downloading an app,driving to a location, taking a photograph of a location or object,visiting a website, viewing or listening to an advertisement, watching avideo, and many others.

In embodiments an AI system/expert system may use or optimize one ormore parameters for a charging plan, such as for charging a battery ofan electric or hybrid vehicle. Charging plan parameters may includerouting (such as to charging locations), amount of charge or fuelprovided, duration of time for charging, battery state, battery chargingprofile, time required to charge, value of charging, indicators ofvalue, market price, bids for charging, available supply capacity (suchas within a geofence or within a range of a set of vehicles), demand(such as based on detected charge/refueling state, based on requesteddemand, or the like), supply, and others. A neural network or othersystem (optionally a hybrid system as describe herein), using a model oralgorithm (such as a genetic algorithm) may be used (such as by beingtrained over a set of trials on outcomes, and/or using a training set ofhuman created or human supervised inputs, or the like) may provide afavorable and/or optimized charging plan for a vehicle or a set ofvehicles based on the parameters. Other inputs may include priority forcertain vehicles (e.g., for emergency responders or for those who havebeen rewarded priority in connection with various embodiments describedherein).

In embodiments a processor, as described herein, may comprise a neuralprocessing chip, such as one employing a fabric, such as a LambdaFabric.Such a chip may have a plurality of cores, such as 256 cores, where eachcore is configured in a neuron-like arrangement with other cores on thesame chip. Each core may comprise a micro-scale digital signalprocessor, and the fabric may enable the cores to readily connect to theother cores on the chip. In embodiments, the fabric may connect a largenumber of cores (e.g., more than 500,000 cores) and/or chips, therebyfacilitating use in computational environments that require, forexample, large scale neural networks, massively parallel computing, andlarge-scale, complex conditional logic. In embodiments, a low-latencyfabric is used, such as one that has latency of 400 nanoseconds, 300nanoseconds, 200 nanoseconds, 100 nanoseconds, or less fromdevice-to-device, rack-to-rack, or the like. The chip may be a low powerchip, such as one that can be powered by energy harvesting from theenvironment, from an inspection signal, from an onboard antenna, or thelike. In embodiments, the cores may be configured to enable applicationof a set of sparse matrix heterogeneous machine learning algorithms. Thechip may run an object-oriented programming language, such as C++, Java,or the like. In embodiments, a chip may be programmed to run each corewith a different algorithm, thereby enabling heterogeneity inalgorithms, such as to enable one or more of the hybrid neural networkembodiments described throughout this disclosure. A chip can therebytake multiple inputs (e.g., one per core) from multiple data sources,undertake massively parallel processing using a large set of distinctalgorithms, and provide a plurality of outputs (such as one per core orper set of cores).

In embodiments a chip may contain or enable a security fabric, such as afabric for performing content inspection, packet inspection (such asagainst a black list, white list, or the like), and the like, inaddition to undertaking processing tasks, such as for a neural network,hybrid AI solution, or the like.

In embodiments, the platform described herein may include, integratewith, or connect with a system for robotic process automation (RPA),whereby an artificial intelligence/machine learning system may betrained on a training set of data that consists of tracking andrecording sets of interactions of humans as the humans interact with aset of interfaces, such as graphical user interfaces (e.g., viainteractions with mouse, trackpad, keyboard, touch screen, joystick,remote control devices); audio system interfaces (such as bymicrophones, smart speakers, voice response interfaces, intelligentagent interfaces (e.g., Siri and Alexa) and the like); human-machineinterfaces (such as involving robotic systems, prosthetics, cyberneticsystems, exoskeleton systems, wearables (including clothing, headgear,headphones, watches, wrist bands, glasses, arm bands, torso bands,belts, rings, necklaces and other accessories); physical or mechanicalinterfaces (e.g., buttons, dials, toggles, knobs, touch screens, levers,handles, steering systems, wheels, and many others); optical interfaces(including ones triggered by eye tracking, facial recognition, gesturerecognition, emotion recognition, and the like); sensor-enabledinterfaces (such as ones involving cameras, EEG or other electricalsignal sensing (such as for brain-computer interfaces), magneticsensing, accelerometers, galvanic skin response sensors, opticalsensors, IR sensors, LIDAR and other sensor sets that are capable ofrecognizing thoughts, gestures (facial, hand, posture, or other),utterances, and the like, and others. In addition to tracking andrecording human interactions, the RPA system may also track and record aset of states, actions, events and results that occur by, within, fromor about the systems and processes with which the humans are engaging.For example, the RPA system may record mouse clicks on a frame of videothat appears within a process by which a human review the video, such aswhere the human highlights points of interest within the video, tagsobjects in the video, captures parameters (such as sizes, dimensions, orthe like), or otherwise operates on the video within a graphical userinterface. The RPA system may also record system or process states andevents, such as recording what elements were the subject of interaction,what the state of a system was before, during and after interaction, andwhat outputs were provided by the system or what results were achieved.Through a large training set of observation of human interactions andsystem states, events, and outcomes, the RPA system may learn tointeract with the system in a fashion that mimics that of the human.Learning may be reinforced by training and supervision, such as byhaving a human correct the RPA system as it attempts in a set of trialsto undertake the action that the human would have undertaken (e.g.,tagging the right object, labeling an item correctly, selecting thecorrect button to trigger a next step in a process, or the like), suchthat over a set of trials the RPA system becomes increasingly effectiveat replicating the action the human would have taken. Learning mayinclude deep learning, such as by reinforcing learning based onoutcomes, such as successful outcomes (such as based on successfulprocess completion, financial yield, and many other outcome measuresdescribed throughout this disclosure). In embodiments, an RPA system maybe seeded during a learning phase with a set of expert humaninteractions, such that the RPA system begins to be able to replicateexpert interaction with a system. For example, an expert driver'sinteractions with a robotic system, such as a remote-controlled vehicleor a UAV, may be recorded along with information about the vehiclesstate (e.g., the surrounding environment, navigation parameters, andpurpose), such that the RPA system may learn to drive the vehicle in away that reflects the same choices as an expert driver. After beingtaught to replicate the skills or expertise of an expert human, the RPAsystem may be transitioned to a deep learning mode, where the systemfurther improves based on a set of outcomes, such as by being configuredto attempt some level of variation in approach (e.g., trying differentnavigation paths to optimize time of arrival, or trying differentapproaches to deceleration and acceleration in curves) and trackingoutcomes (with feedback), such that the RPA system can learn, byvariation/experimentation (which may be randomized, rule-based, or thelike, such as using genetic programming techniques, random-walktechniques, random forest techniques, and others) and selection, toexceed the expertise of the human expert. Thus, the RPA system learnsfrom a human expert, acquires expertise in interacting with a system orprocess, facilitates automation of the process (such as by taking oversome of the more repetitive tasks, including ones that requireconsistent execution of acquired skills), and provides a very effectiveseed for artificial intelligence, such as by providing a seed model orsystem that can be improved by machine learning with feedback onoutcomes of a system or process.

RPA systems may have particular value in situations where humanexpertise or knowledge is acquired with training and experience, as wellas in situations where the human brain and sensory systems areparticularly adapted and evolved to solve problems that arecomputationally difficult or highly complex. Thus, in embodiments, RPAsystems may be used to learn to undertake, among other things: visualpattern recognition tasks with respect to the various systems,processes, workflows and environments described herein (such asrecognizing the meaning of dynamic interactions of objects or entitieswithin a video stream (e.g., to understand what is taking place ashumans and objects interact in a video); recognition of the significanceof visual patterns (e.g., recognizing objects, structures, defects andconditions in a photograph or radiography image); tagging of relevantobjects within a visual pattern (e.g., tagging or labeling objects bytype, category, or specific identity (such as person recognition);indication of metrics in a visual pattern (such as dimensions of objectsindicated by clicking on dimensions in an x-ray or the like); labelingactivities in a visual pattern by category (e.g., what work process isbeing done); recognizing a pattern that is displayed as a signal (e.g.,a wave or similar pattern in a frequency domain, time domain, or othersignal processing representation); anticipate a n future state based ona current state (e.g., anticipating motion of a flying or rollingobject, anticipating a next action by a human in a process, anticipatinga next step by a machine, anticipating a reaction by a person to anevent, and many others); recognize and predicting emotional states andreactions (such as based on facial expression, posture, body language orthe like); apply a heuristic to achieve a favorable state withoutdeterministic calculation (e.g., selecting a favorable strategy in sportor game, selecting a business strategy, selecting a negotiatingstrategy, setting a price for a product, developing a message to promotea product or idea, generating creative content, recognizing a favorablestyle or fashion, and many others); any many others. In embodiments, anRPA system may automate workflows that involve visual inspection ofpeople, systems, and objects (including internal components), workflowsthat involve performing software tasks, such as involving sequentialinteractions with a series of screens in a software interface, workflowsthat involve remote control of robots and other systems and devices,workflows that involve content creation (such as selecting, editing andsequencing content), workflows that involve financial decision-makingand negotiation (such as setting prices and other terms and conditionsof financial and other transactions), workflows that involvedecision-making (such as selecting an optimal configuration for a systemor sub-system, selecting an optimal path or sequence of actions in aworkflow, process or other activity that involves dynamicdecision-making), and many others.

In embodiments, an RPA system may use a set of IoT devices and systems(such as cameras and sensors), to track and record human actions andinteractions with respect to various interfaces and systems in anenvironment. The RPA system may also use data from onboard sensors,telemetry, and event recording systems, such as telemetry systems onvehicles and event logs on computers). The RPA system may thus generateand/or receive a large data set (optionally distributed) for anenvironment (such as any of the environments described throughout thisdisclosure) including data recording the various entities (human andnon-human), systems, processes, applications (e.g., softwareapplications used to enable workflows), states, events, and outcomes,which can be used to train the RPA system (or a set of RPA systemsdedicated to automating various processes and workflows) to accomplishprocesses and workflows in a way that reflects and mimics accumulatedhuman expertise, and that eventually improves on the results of thathuman expertise by further machine learning.

Referring to FIG. 9 , in embodiments provided herein are transportationsystems 911 having an artificial intelligence system 936 that uses atleast one genetic algorithm 975 to explore a set of possible vehicleoperating states 945 to determine at least one optimized operatingstate. In embodiments, the genetic algorithm 975 takes inputs relatingto at least one vehicle performance parameter 982 and at least one riderstate 937.

An aspect provided herein includes a system for transportation 911,comprising: a vehicle 910 having a vehicle operating state 945; anartificial intelligence system 936 to execute a genetic algorithm 975 togenerate mutations from an initial vehicle operating state to determineat least one optimized vehicle operating state. In embodiments, thevehicle operating state 945 includes a set of vehicle parameter values984. In embodiments, the genetic algorithm 975 is to: vary the set ofvehicle parameter values 984 for a set of corresponding time periodssuch that the vehicle 910 operates according to the set of vehicleparameter values 984 during the corresponding time periods; evaluate thevehicle operating state 945 for each of the corresponding time periodsaccording to a set of measures 983 to generate evaluations; and select,for future operation of the vehicle 910, an optimized set of vehicleparameter values based on the evaluations.

In embodiments, the vehicle operating state 945 includes the rider state937 of a rider of the vehicle. In embodiments, the at least oneoptimized vehicle operating state includes an optimized state of therider. In embodiments, the genetic algorithm 975 is to optimize thestate of the rider. In embodiments, the evaluating according to the setof measures 983 is to determine the state of the rider corresponding tothe vehicle parameter values 984.

In embodiments, the vehicle operating state 945 includes a state of therider of the vehicle. In embodiments, the set of vehicle parametervalues 984 includes a set of vehicle performance control values. Inembodiments, the at least one optimized vehicle operating state includesan optimized state of performance of the vehicle. In embodiments, thegenetic algorithm 975 is to optimize the state of the rider and thestate of performance of the vehicle. In embodiments, the evaluatingaccording to the set of measures 983 is to determine the state of therider and the state of performance of the vehicle corresponding to thevehicle performance control values.

In embodiments, the set of vehicle parameter values 984 includes a setof vehicle performance control values. In embodiments, the at least oneoptimized vehicle operating state includes an optimized state ofperformance of the vehicle. In embodiments, the genetic algorithm 975 isto optimize the state of performance of the vehicle. In embodiments, theevaluating according to the set of measures 983 is to determine thestate of performance of the vehicle corresponding to the vehicleperformance control values.

In embodiments, the set of vehicle parameter values 984 includes arider-occupied parameter value. In embodiments, the rider-occupiedparameter value affirms a presence of a rider in the vehicle 910. Inembodiments, the vehicle operating state 945 includes the rider state937 of a rider of the vehicle. In embodiments, the at least oneoptimized vehicle operating state includes an optimized state of therider. In embodiments, the genetic algorithm 975 is to optimize thestate of the rider. In embodiments, the evaluating according to the setof measures 983 is to determine the state of the rider corresponding tothe vehicle parameter values 984. In embodiments, the state of the riderincludes a rider satisfaction parameter. In embodiments, the state ofthe rider includes an input representative of the rider. In embodiments,the input representative of the rider is selected from the groupconsisting of: a rider state parameter, a rider comfort parameter, arider emotional state parameter, a rider satisfaction parameter, a ridergoals parameter, a classification of trip, and combinations thereof.

In embodiments, the set of vehicle parameter values 984 includes a setof vehicle performance control values. In embodiments, the at least oneoptimized vehicle operating state includes an optimized state ofperformance of the vehicle. In embodiments, the genetic algorithm 975 isto optimize the state of the rider and the state of performance of thevehicle. In embodiments, the evaluating according to the set of measures983 is to determine the state of the rider and the state of performanceof the vehicle corresponding to the vehicle performance control values.In embodiments, the set of vehicle parameter values 984 includes a setof vehicle performance control values. In embodiments, the at least oneoptimized vehicle operating state includes an optimized state ofperformance of the vehicle. In embodiments, the genetic algorithm 975 isto optimize the state of performance of the vehicle. In embodiments, theevaluating according to the set of measures 983 is to determine thestate of performance of the vehicle corresponding to the vehicleperformance control values.

In embodiments, the set of vehicle performance control values areselected from the group consisting of: a fuel efficiency; a tripduration; a vehicle wear; a vehicle make; a vehicle model; a vehicleenergy consumption profiles; a fuel capacity; a real-time fuel levels; acharge capacity; a recharging capability; a regenerative braking state;and combinations thereof. In embodiments, at least a portion of the setof vehicle performance control values is sourced from at least one of anon-board diagnostic system, a telemetry system, a software system, avehicle-located sensor, and a system external to the vehicle 910. Inembodiments, the set of measures 983 relates to a set of vehicleoperating criteria. In embodiments, the set of measures 983 relates to aset of rider satisfaction criteria. In embodiments, the set of measures983 relates to a combination of vehicle operating criteria and ridersatisfaction criteria. In embodiments, each evaluation uses feedbackindicative of an effect on at least one of a state of performance of thevehicle and a state of the rider.

An aspect provided herein includes a system for transportation 911,comprising: an artificial intelligence system 936 to process inputsrepresentative of a state of a vehicle and inputs representative of arider state 937 of a rider occupying the vehicle during the state of thevehicle with the genetic algorithm 975 to optimize a set of vehicleparameters that affects the state of the vehicle or the rider state 937.In embodiments, the genetic algorithm 975 is to perform a series ofevaluations using variations of the inputs. In embodiments, eachevaluation in the series of evaluations uses feedback indicative of aneffect on at least one of a vehicle operating state 945 and the riderstate 937. In embodiments, the inputs representative of the rider state937 indicate that the rider is absent from the vehicle 910. Inembodiments, the state of the vehicle includes the vehicle operatingstate 945. In embodiments, a vehicle parameter in the set of vehicleparameters includes a vehicle performance parameter 982. In embodiments,the genetic algorithm 975 is to optimize the set of vehicle parametersfor the state of the rider.

In embodiments, optimizing the set of vehicle parameters is responsiveto an identifying, by the genetic algorithm 975, of at least one vehicleparameter that produces a favorable rider state. In embodiments, thegenetic algorithm 975 is to optimize the set of vehicle parameters forvehicle performance. In embodiments, the genetic algorithm 975 is tooptimize the set of vehicle parameters for the state of the rider and isto optimize the set of vehicle parameters for vehicle performance. Inembodiments, optimizing the set of vehicle parameters is responsive tothe genetic algorithm 975 identifying at least one of a favorablevehicle operating state, and favorable vehicle performance thatmaintains the rider state 937. In embodiments, the artificialintelligence system 936 further includes a neural network selected froma plurality of different neural networks. In embodiments, the selectionof the neural network involves the genetic algorithm 975. Inembodiments, the selection of the neural network is based on astructured competition among the plurality of different neural networks.In embodiments, the genetic algorithm 975 facilitates training a neuralnetwork to process interactions among a plurality of vehicle operatingsystems and riders to produce the optimized set of vehicle parameters.

In embodiments, a set of inputs relating to at least one vehicleparameter are provided by at least one of an on-board diagnostic system,a telemetry system, a vehicle-located sensor, and a system external tothe vehicle. In embodiments, the inputs representative of the riderstate 937 comprise at least one of comfort, emotional state,satisfaction, goals, classification of trip, or fatigue. In embodiments,the inputs representative of the rider state 937 reflect a satisfactionparameter of at least one of a driver, a fleet manager, an advertiser, amerchant, an owner, an operator, an insurer, and a regulator. Inembodiments, the inputs representative of the rider state 937 compriseinputs relating to a user that, when processed with a cognitive systemyield the rider state 937.

Referring to FIG. 10 , in embodiments provided herein are transportationsystems 1011 having a hybrid neural network 1047 for optimizing theoperating state of a continuously variable powertrain 1013 of a vehicle1010. In embodiments, at least one part of the hybrid neural network1047 operates to classify a state of the vehicle 1010 and another partof the hybrid neural network 1047 operates to optimize at least oneoperating parameter 1060 of the transmission 1019. In embodiments, thevehicle 1010 may be a self-driving vehicle. In an example, the firstportion 1085 of the hybrid neural network may classify the vehicle 1010as operating in a high-traffic state (such as by use of LIDAR, RADAR, orthe like that indicates the presence of other vehicles, or by takinginput from a traffic monitoring system, or by detecting the presence ofa high density of mobile devices, or the like) and a bad weather state(such as by taking inputs indicating wet roads (such as usingvision-based systems), precipitation (such as determined by radar),presence of ice (such as by temperature sensing, vision-based sensing,or the like), hail (such as by impact detection, sound-sensing, or thelike), lightning (such as by vision-based systems, sound-based systems,or the like), or the like. Once classified, another neural network 1086(optionally of another type) may optimize the vehicle operatingparameter based on the classified state, such as by putting the vehicle1010 into a safe-driving mode (e.g., by providing forward-sensing alertsat greater distances and/lower speeds than in good weather, by providingautomated braking earlier and more aggressively than in good weather,and the like).

An aspect provided herein includes a system for transportation 1011,comprising: a hybrid neural network 1047 for optimizing an operatingstate of a continuously variable powertrain 1013 of a vehicle 1010. Inembodiments, a portion 1085 of the hybrid neural network 1047 is tooperate to classify a state 1044 of the vehicle 1010 thereby generatinga classified state of the vehicle, and an other portion 1086 of thehybrid neural network 1047 is to operate to optimize at least oneoperating parameter 1060 of a transmission 1019 portion of thecontinuously variable powertrain 1013.

In embodiments, the system for transportation 1011 further comprises: anartificial intelligence system 1036 operative on at least one processor1088, the artificial intelligence system 1036 to operate the portion1085 of the hybrid neural network 1047 to operate to classify the stateof the vehicle and the artificial intelligence system 1036 to operatethe other portion 1086 of the hybrid neural network 1047 to optimize theat least one operating parameter 1087 of the transmission 1019 portionof the continuously variable powertrain 1013 based on the classifiedstate of the vehicle. In embodiments, the vehicle 1010 comprises asystem for automating at least one control parameter of the vehicle. Inembodiments, the vehicle 1010 is at least a semi-autonomous vehicle. Inembodiments, the vehicle 1010 is to be automatically routed. Inembodiments, the vehicle 1010 is a self-driving vehicle. In embodiments,the classified state of the vehicle is: a vehicle maintenance state; avehicle health state; a vehicle operating state; a vehicle energyutilization state; a vehicle charging state; a vehicle satisfactionstate; a vehicle component state; a vehicle sub-system state; a vehiclepowertrain system state; a vehicle braking system state; a vehicleclutch system state; a vehicle lubrication system state; a vehicletransportation infrastructure system state; or a vehicle rider state. Inembodiments, at least a portion of the hybrid neural network 1047 is aconvolutional neural network.

FIG. 11 illustrates a method 1100 for optimizing operation of acontinuously variable vehicle powertrain of a vehicle in accordance withembodiments of the systems and methods disclosed herein. At 1102, themethod includes executing a first network of a hybrid neural network onat least one processor, the first network classifying a plurality ofoperational states of the vehicle. In embodiments, at least a portion ofthe operational states is based on a state of the continuously variablepowertrain of the vehicle. At 1104, the method includes executing asecond network of the hybrid neural network on the at least oneprocessor, the second network processing inputs that are descriptive ofthe vehicle and of at least one detected condition associated with anoccupant of the vehicle for at least one of the plurality of classifiedoperational states of the vehicle. In embodiments, the processing theinputs by the second network causes optimization of at least oneoperating parameter of the continuously variable powertrain of thevehicle for a plurality of the operational states of the vehicle.

Referring to FIG. 10 and FIG. 11 together, in embodiments, the vehiclecomprises an artificial intelligence system 1036, the method furthercomprising automating at least one control parameter of the vehicle bythe artificial intelligence system 1036. In embodiments, the vehicle1010 is at least a semi-autonomous vehicle. In embodiments, the vehicle1010 is to be automatically routed. In embodiments, the vehicle 1010 isa self-driving vehicle. In embodiments, the method further comprisesoptimizing, by the artificial intelligence system 1036, an operatingstate of the continuously variable powertrain 1013 of the vehicle basedon the optimized at least one operating parameter 1060 of thecontinuously variable powertrain 1013 by adjusting at least one otheroperating parameter 1087 of a transmission 1019 portion of thecontinuously variable powertrain 1013.

In embodiments, the method further comprises optimizing, by theartificial intelligence system 1036, the operating state of thecontinuously variable powertrain 1013 by processing social data from aplurality of social data sources. In embodiments, the method furthercomprises optimizing, by the artificial intelligence system 1036, theoperating state of the continuously variable powertrain 1013 byprocessing data sourced from a stream of data from unstructured datasources. In embodiments, the method further comprises optimizing, by theartificial intelligence system 1036, the operating state of thecontinuously variable powertrain 1013 by processing data sourced fromwearable devices. In embodiments, the method further comprisesoptimizing, by the artificial intelligence system 1036, the operatingstate of the continuously variable powertrain 1013 by processing datasourced from in-vehicle sensors. In embodiments, the method furthercomprises optimizing, by the artificial intelligence system 1036, theoperating state of the continuously variable powertrain 1013 byprocessing data sourced from a rider helmet.

In embodiments, the method further comprises optimizing, by theartificial intelligence system 1036, the operating state of thecontinuously variable powertrain 1013 by processing data sourced fromrider headgear. In embodiments, the method further comprises optimizing,by the artificial intelligence system 1036, the operating state of thecontinuously variable powertrain 1013 by processing data sourced from arider voice system. In embodiments, the method further comprisesoperating, by the artificial intelligence system 1036, a third networkof the hybrid neural network 1047 to predict a state of the vehiclebased at least in part on at least one of the classified plurality ofoperational states of the vehicle and at least one operating parameterof the transmission 1019. In embodiments, the first network of thehybrid neural network 1047 comprises a structure-adaptive network toadapt a structure of the first network responsive to a result ofoperating the first network of the hybrid neural network 1047. Inembodiments, the first network of the hybrid neural network 1047 is toprocess a plurality of social data from social data sources to classifythe plurality of operational states of the vehicle.

In embodiments, at least a portion of the hybrid neural network 1047 isa convolutional neural network. In embodiments, at least one of theclassified plurality of operational states of the vehicle is: a vehiclemaintenance state; or a vehicle health state. In embodiments, at leastone of the classified states of the vehicle is: a vehicle operatingstate; a vehicle energy utilization state; a vehicle charging state; avehicle satisfaction state; a vehicle component state; a vehiclesub-system state; a vehicle powertrain system state; a vehicle brakingsystem state; a vehicle clutch system state; a vehicle lubricationsystem state; or a vehicle transportation infrastructure system state.In embodiments, the at least one of classified states of the vehicle isa vehicle driver state. In embodiments, the at least one of classifiedstates of the vehicle is a vehicle rider state.

Referring to FIG. 12 , in embodiments, provided herein aretransportation systems 1211 having a cognitive system for routing atleast one vehicle 1210 within a set of vehicles 1294 based on a routingparameter determined by facilitating negotiation among a designated setof vehicles. In embodiments, negotiation accepts inputs relating to thevalue attributed by at least one rider to at least one parameter 1230 ofa route 1295. A user 1290 may express value by a user interface thatrates one or more parameters (e.g., any of the parameters notedthroughout), by behavior (e.g., undertaking behavior that reflects orindicates value ascribed to arriving on time, following a given route1295, or the like), or by providing or offering value (e.g., offeringcurrency, tokens, points, cryptocurrency, rewards, or the like). Forexample, a user 1290 may negotiate for a preferred route by offeringtokens to the system that are awarded if the user 1290 arrives at adesignated time, while others may offer to accept tokens in exchange fortaking alternative routes (and thereby reducing congestion). Thus, anartificial intelligence system may optimize a combination of offers toprovide rewards or to undertake behavior in response to rewards, suchthat the reward system optimizes a set of outcomes. Negotiation mayinclude explicit negotiation, such as where a driver offers to rewarddrivers ahead of the driver on the road in exchange for their leavingthe route temporarily as the driver passes.

An aspect provided herein includes a system for transportation 1211,comprising: a cognitive system for routing at least one vehicle 1210within a set of vehicles 1294 based on a routing parameter determined byfacilitating a negotiation among a designated set of vehicles, whereinthe negotiation accepts inputs relating to a value attributed by atleast one user 1290 to at least one parameter of a route 1295.

FIG. 13 illustrates a method 1300 of negotiation-based vehicle routingin accordance with embodiments of the systems and methods disclosedherein. At 1302, the method includes facilitating a negotiation of aroute-adjustment value for a plurality of parameters used by a vehiclerouting system to route at least one vehicle in a set of vehicles. At1304, the method includes determining a parameter in the plurality ofparameters for optimizing at least one outcome based on the negotiation.

Referring to FIG. 12 and FIG. 13 , in embodiments, a user 1290 is anadministrator for a set of roadways to be used by the at least onevehicle 1210 in the set of vehicles 1294. In embodiments, a user 1290 isan administrator for a fleet of vehicles including the set of vehicles1294. In embodiments, the method further comprises offering a set ofoffered user-indicated values for the plurality of parameters 1230 tousers 1290 with respect to the set of vehicles 1294. In embodiments, theroute-adjustment value 1224 is based at least in part on the set ofoffered user-indicated values 1297. In embodiments, the route-adjustmentvalue 1224 is further based on at least one user response to theoffering. In embodiments, the route-adjustment value 1224 is based atleast in part on the set of offered user-indicated values 1297 and atleast one response thereto by at least one user of the set of vehicles1294. In embodiments, the determined parameter facilitates adjusting aroute 1295 of at least one of the vehicles 1210 in the set of vehicles1294. In embodiments, adjusting the route includes prioritizing thedetermined parameter for use by the vehicle routing system.

In embodiments, the facilitating negotiation includes facilitatingnegotiation of a price of a service. In embodiments, the facilitatingnegotiation includes facilitating negotiation of a price of fuel. Inembodiments, the facilitating negotiation includes facilitatingnegotiation of a price of recharging. In embodiments, the facilitatingnegotiation includes facilitating negotiation of a reward for taking arouting action.

An aspect provided herein includes a transportation system 1211 fornegotiation-based vehicle routing comprising: a route adjustmentnegotiation system 1289 through which users 1290 in a set of users 1291negotiate a route-adjustment value 1224 for at least one of a pluralityof parameters 1230 used by a vehicle routing system 1292 to route atleast one vehicle 1210 in a set of vehicles 1294; and a user routeoptimizing circuit 1293 to optimize a portion of a route 1295 of atleast one user 1290 of the set of vehicles 1294 based on theroute-adjustment value 1224 for the at least one of the plurality ofparameters 1230. In embodiments, the route-adjustment value 1224 isbased at least in part on user-indicated values 1297 and at least onenegotiation response thereto by at least one user of the set of vehicles1294. In embodiments, the transportation system 1211 further comprises avehicle-based route negotiation interface through which user-indicatedvalues 1297 for the plurality of parameters 1230 used by the vehiclerouting system are captured. In embodiments, a user 1290 is a rider ofthe at least one vehicle 1210. In embodiments, a user 1290 is anadministrator for a set of roadways to be used by the at least onevehicle 1210 in the set of vehicles 1294.

In embodiments, a user 1290 is an administrator for a fleet of vehiclesincluding the set of vehicles 1294. In embodiments, the at least one ofthe plurality of parameters 1230 facilitates adjusting a route 1295 ofthe at least one vehicle 1210. In embodiments, adjusting the route 1295includes prioritizing a determined parameter for use by the vehiclerouting system. In embodiments, at least one of the user-indicatedvalues 1297 is attributed to at least one of the plurality of parameters1230 through an interface to facilitate expression of rating one or moreroute parameters. In embodiments, the vehicle-based route negotiationinterface facilitates expression of rating one or more route parameters.In embodiments, the user-indicated values 1297 are derived from abehavior of the user 1290. In embodiments, the vehicle-based routenegotiation interface facilitates converting user behavior to theuser-indicated values 1297. In embodiments, the user behavior reflectsvalue ascribed to the at least one parameter used by the vehicle routingsystem to influence a route 1295 of at least one vehicle 1210 in the setof vehicles 1294. In embodiments, the user-indicated value indicated byat least one user 1290 correlates to an item of value provided by theuser 1290. In embodiments, the item of value is provided by the user1290 through an offering of the item of value in exchange for a resultof routing based on the at least one parameter. In embodiments, thenegotiating of the route-adjustment value 1224 includes offering an itemof value to the users of the set of vehicles 1294.

Referring to FIG. 14 , in embodiments provided herein are transportationsystems 1411 having a cognitive system for routing at least one vehicle1410 within a set of vehicles 1494 based on a routing parameterdetermined by facilitating coordination among a designated set ofvehicles 1498. In embodiments, the coordination is accomplished bytaking at least one input from at least one game-based interface 1499for riders of the vehicles. A game-based interface 1499 may includerewards for undertaking game-like actions (i.e., game activities 14101)that provide an ancillary benefit. For example, a rider in a vehicle1410 may be rewarded for routing the vehicle 1410 to a point of interestoff a highway (such as to collect a coin, to capture an item, or thelike), while the rider's departure clears space for other vehicles thatare seeking to achieve other objectives, such as on-time arrival. Forexample, a game like Pokemon Go™ may be configured to indicate thepresence of rare Pokemon™ creatures in locations that attract trafficaway from congested locations. Others may provide rewards (e.g.,currency, cryptocurrency or the like) that may be pooled to attractusers 1490 away from congested roads.

An aspect provided herein includes a system for transportation 1411,comprising: a cognitive system for routing at least one vehicle 1410within a set of vehicles 1494 based on a set of routing parameters 1430determined by facilitating coordination among a designated set ofvehicles 1498, wherein the coordination is accomplished by taking atleast one input from at least one game-based interface 1499 for a user1490 of a vehicle 1410 in the designated set of vehicles 1498.

In embodiments, the system for transportation further comprises: avehicle routing system 1492 to route the at least one vehicle 1410 basedon the set of routing parameters 1430; and the game-based interface 1499through which the user 1490 indicates a routing preference 14100 for atleast one vehicle 1410 within the set of vehicles 1494 to undertake agame activity 14101 offered in the game-based interface 1499; whereinthe game-based interface 1499 is to induce the user 1490 to undertake aset of favorable routing choices based on the set of routing parameters1430. As used herein, “to route” means to select a route 1495.

In embodiments, the vehicle routing system 1492 accounts for the routingpreference 14100 of the user 1490 when routing the at least one vehicle1410 within the set of vehicles 1494. In embodiments, the game-basedinterface 1499 is disposed for in-vehicle use as indicated in FIG. 14 bythe line extending from the Game-Based Interface into the box forVehicle 1. In embodiments, the user 1490 is a rider of the at least onevehicle 1410. In embodiments, the user 1490 is an administrator for aset of roadways to be used by the at least one vehicle 1410 in the setof vehicles 1494. In embodiments, the user 1490 is an administrator fora fleet of vehicles including the set of vehicles 1494. In embodiments,the set of routing parameters 1430 includes at least one of trafficcongestion, desired arrival times, preferred routes, fuel efficiency,pollution reduction, accident avoidance, avoiding bad weather, avoidingbad road conditions, reduced fuel consumption, reduced carbon footprint,reduced noise in a region, avoiding high-crime regions, collectivesatisfaction, maximum speed limit, avoidance of toll roads, avoidance ofcity roads, avoidance of undivided highways, avoidance of left turns,avoidance of driver-operated vehicles. In embodiments, the game activity14101 offered in the game-based interface 1499 includes contests. Inembodiments, the game activity 14101 offered in the game-based interface1499 includes entertainment games.

In embodiments, the game activity 14101 offered in the game-basedinterface 1499 includes competitive games. In embodiments, the gameactivity 14101 offered in the game-based interface 1499 includesstrategy games. In embodiments, the game activity 14101 offered in thegame-based interface 1499 includes scavenger hunts. In embodiments, theset of favorable routing choices is configured so that the vehiclerouting system 1492 achieves a fuel efficiency objective. Inembodiments, the set of favorable routing choices is configured so thatthe vehicle routing system 1492 achieves a reduced traffic objective. Inembodiments, the set of favorable routing choices is configured so thatthe vehicle routing system 1492 achieves a reduced pollution objective.In embodiments, the set of favorable routing choices is configured sothat the vehicle routing system 1492 achieves a reduced carbon footprintobjective.

In embodiments, the set of favorable routing choices is configured sothat the vehicle routing system 1492 achieves a reduced noise inneighborhoods objective. In embodiments, the set of favorable routingchoices is configured so that the vehicle routing system 1492 achieves acollective satisfaction objective. In embodiments, the set of favorablerouting choices is configured so that the vehicle routing system 1492achieves an avoiding accident scenes objective. In embodiments, the setof favorable routing choices is configured so that the vehicle routingsystem 1492 achieves an avoiding high-crime areas objective. Inembodiments, the set of favorable routing choices is configured so thatthe vehicle routing system 1492 achieves a reduced traffic congestionobjective. In embodiments, the set of favorable routing choices isconfigured so that the vehicle routing system 1492 achieves a badweather avoidance objective.

In embodiments, the set of favorable routing choices is configured sothat the vehicle routing system 1492 achieves a maximum travel timeobjective. In embodiments, the set of favorable routing choices isconfigured so that the vehicle routing system 1492 achieves a maximumspeed limit objective. In embodiments, the set of favorable routingchoices is configured so that the vehicle routing system 1492 achievesan avoidance of toll roads objective. In embodiments, the set offavorable routing choices is configured so that the vehicle routingsystem 1492 achieves an avoidance of city roads objective. Inembodiments, the set of favorable routing choices is configured so thatthe vehicle routing system 1492 achieves an avoidance of undividedhighways objective. In embodiments, the set of favorable routing choicesis configured so that the vehicle routing system 1492 achieves anavoidance of left turns objective. In embodiments, the set of favorablerouting choices is configured so that the vehicle routing system 1492achieves an avoidance of driver-operated vehicles objective.

FIG. 15 illustrates a method 1500 of game-based coordinated vehiclerouting in accordance with embodiments of the systems and methodsdisclosed herein. At 1502, the method includes presenting, in agame-based interface, a vehicle route preference-affecting gameactivity. At 1504, the method includes receiving, through the game-basedinterface, a user response to the presented game activity. At 1506, themethod includes adjusting a routing preference for the user responsiveto the received response. At 1508, the method includes determining atleast one vehicle-routing parameter used to route vehicles to reflectthe adjusted routing preference for routing vehicles. At 1509, themethod includes routing, with a vehicle routing system, vehicles in aset of vehicles responsive to the at least one determined vehiclerouting parameter adjusted to reflect the adjusted routing preference,wherein routing of the vehicles includes adjusting the determinedrouting parameter for at least a plurality of vehicles in the set ofvehicles.

Referring to FIG. 14 and FIG. 15 , in embodiments, the method furthercomprises indicating, by the game-based interface 1499, a reward value14102 for accepting the game activity 14101. In embodiments, thegame-based interface 1499 further comprises a routing preferencenegotiation system 1436 for a rider to negotiate the reward value 14102for accepting the game activity 14101. In embodiments, the reward value14102 is a result of pooling contributions of value from riders in theset of vehicles. In embodiments, at least one routing parameter 1430used by the vehicle routing system 1492 to route the vehicles 1410 inthe set of vehicles 1494 is associated with the game activity 14101 anda user acceptance of the game activity 14101 adjusts (e.g., by therouting adjustment value 1424) the at least one routing parameter 1430to reflect the routing preference. In embodiments, the user response tothe presented game activity 14101 is derived from a user interactionwith the game-based interface 1499. In embodiments, the at least onerouting parameter used by the vehicle routing system 1492 to route thevehicles 1410 in the set of vehicles 1494 includes at least one of:traffic congestion, desired arrival times, preferred routes, fuelefficiency, pollution reduction, accident avoidance, avoiding badweather, avoiding bad road conditions, reduced fuel consumption, reducedcarbon footprint, reduced noise in a region, avoiding high-crimeregions, collective satisfaction, maximum speed limit, avoidance of tollroads, avoidance of city roads, avoidance of undivided highways,avoidance of left turns, and avoidance of driver-operated vehicles.

In embodiments, the game activity 14101 presented in the game-basedinterface 1499 includes contests. In embodiments, the game activity14101 presented in the game-based interface 1499 includes entertainmentgames. In embodiments, the game activity 14101 presented in thegame-based interface 1496 includes competitive games. In embodiments,the game activity 14101 presented in the game-based interface 1499includes strategy games. In embodiments, the game activity 14101presented in the game-based interface 1499 includes scavenger hunts. Inembodiments, the routing responsive to the at least one determinedvehicle routing parameter 14103 achieves a fuel efficiency objective. Inembodiments, the routing responsive to the at least one determinedvehicle routing parameter 14103 achieves a reduced traffic objective.

In embodiments, the routing responsive to the at least one determinedvehicle routing parameter 14103 achieves a reduced pollution objective.In embodiments, the routing responsive to the at least one determinedvehicle routing parameter 14103 achieves a reduced carbon footprintobjective. In embodiments, the routing responsive to the at least onedetermined vehicle routing parameter 14103 achieves a reduced noise inneighborhoods objective. In embodiments, the routing responsive to theat least one determined vehicle routing parameter 14103 achieves acollective satisfaction objective. In embodiments, the routingresponsive to the at least one determined vehicle routing parameter14103 achieves an avoiding accident scenes objective. In embodiments,the routing responsive to the at least one determined vehicle routingparameter 14103 achieves an avoiding high-crime areas objective. Inembodiments, the routing responsive to the at least one determinedvehicle routing parameter 14103 achieves a reduced traffic congestionobjective.

In embodiments, the routing responsive to the at least one determinedvehicle routing parameter 14103 achieves a bad weather avoidanceobjective. In embodiments, the routing responsive to the at least onedetermined vehicle routing parameter 14103 achieves a maximum traveltime objective. In embodiments, the routing responsive to the at leastone determined vehicle routing parameter 14103 achieves a maximum speedlimit objective. In embodiments, the routing responsive to the at leastone determined vehicle routing parameter 14103 achieves an avoidance oftoll roads objective. In embodiments, the routing responsive to the atleast one determined vehicle routing parameter 14103 achieves anavoidance of city roads objective. In embodiments, the routingresponsive to the at least one determined vehicle routing parameter14103 achieves an avoidance of undivided highways objective. Inembodiments, the routing responsive to the at least one determinedvehicle routing parameter 14103 achieves an avoidance of left turnsobjective. In embodiments, the routing responsive to the at least onedetermined vehicle routing parameter 14103 achieves an avoidance ofdriver-operated vehicles objective.

In embodiments, provided herein are transportation systems 1611 having acognitive system for routing at least one vehicle, wherein the routingis determined at least in part by processing at least one input from arider interface wherein a rider can obtain a reward 16102 by undertakingan action while in the vehicle. In embodiments, the rider interface maydisplay a set of available rewards for undertaking various actions, suchthat the rider may select (such as by interacting with a touch screen oraudio interface), a set of rewards to pursue, such as by allowing anavigation system of the vehicle (or of a ride-share system of which theuser 1690 has at least partial control) or a routing system 1692 of aself-driving vehicle to use the actions that result in rewards to governrouting. For example, selection of a reward for attending a site mayresult in sending a signal to a navigation or routing system 1692 to setan intermediate destination at the site. As another example, indicatinga willingness to watch a piece of content may cause a routing system1692 to select a route that permits adequate time to view or hear thecontent.

An aspect provided herein includes a system for transportation 1611,comprising: a cognitive system for routing at least one vehicle 1610,wherein the routing is based, at least in part, by processing at leastone input from a rider interface, wherein a reward 16102 is madeavailable to a rider in response to the rider undertaking apredetermined action while in the at least one vehicle 1610.

An aspect provided herein includes a transportation system 1611 forreward-based coordinated vehicle routing comprising: a reward-basedinterface 16104 to offer a reward 16102 and through which a user 1690related to a set of vehicles 1694 indicates a routing preference of theuser 1690 related to the reward 16102 by responding to the reward 16102offered in the reward-based interface 16104; a reward offer responseprocessing circuit 16105 to determine at least one user action resultingfrom the user response to the reward 16102 and to determine acorresponding effect 16106 on at least one routing parameter 1630; and avehicle routing system 1692 to use the routing preference 16100 of theuser 1690 and the corresponding effect on the at least one routingparameter to govern routing of the set of vehicles 1694.

In embodiments, the user 1690 is a rider of at least one vehicle 1610 inthe set of vehicles 1694. In embodiments, the user 1690 is anadministrator for a set of roadways to be used by at least one vehicle1610 in the set of vehicles 1694. In embodiments, the user 1690 is anadministrator for a fleet of vehicles including the set of vehicles1694. In embodiments, the reward-based interface 16104 is disposed forin-vehicle use. In embodiments, the at least one routing parameter 1630includes at least one of: traffic congestion, desired arrival times,preferred routes, fuel efficiency, pollution reduction, accidentavoidance, avoiding bad weather, avoiding bad road conditions, reducedfuel consumption, reduced carbon footprint, reduced noise in a region,avoiding high-crime regions, collective satisfaction, maximum speedlimit, avoidance of toll roads, avoidance of city roads, avoidance ofundivided highways, avoidance of left turns, and avoidance ofdriver-operated vehicles. In embodiments, the vehicle routing system1692 is to use the routing preference of the user 1690 and thecorresponding effect on the at least one routing parameter to governrouting of the set of vehicles to achieve a fuel efficiency objective.In embodiments, the vehicle routing system 1692 is to use the routingpreference of the user 1690 and the corresponding effect on the at leastone routing parameter to govern routing of the set of vehicles toachieve a reduced traffic objective. In embodiments, the vehicle routingsystem 1692 is to use the routing preference of the user 1690 and thecorresponding effect on the at least one routing parameter to governrouting of the set of vehicles to achieve′ a reduced pollutionobjective. In embodiments, the vehicle routing system 1692 is to use therouting preference of the user 1690 and the corresponding effect on theat least one routing parameter to govern routing of the set of vehiclesto achieve a reduced carbon footprint objective.

In embodiments, the vehicle routing system 1692 is to use the routingpreference of the user 1690 and the corresponding effect on the at leastone routing parameter to govern routing of the set of vehicles toachieve a reduced noise in neighborhoods objective. In embodiments, thevehicle routing system 1692 is to use the routing preference of the user1690 and the corresponding effect on the at least one routing parameterto govern routing of the set of vehicles to achieve a collectivesatisfaction objective. In embodiments, the vehicle routing system 1692is to use the routing preference of the user 1690 and the correspondingeffect on the at least one routing parameter to govern routing of theset of vehicles to achieve′ an avoiding accident scenes objective. Inembodiments, the vehicle routing system 1692 is to use the routingpreference of the user 1690 and the corresponding effect on the at leastone routing parameter to govern routing of the set of vehicles toachieve an avoiding high-crime areas objective. In embodiments, thevehicle routing system 1692 is to use the routing preference of the user1690 and the corresponding effect on the at least one routing parameterto govern routing of the set of vehicles to achieve a reduced trafficcongestion objective.

In embodiments, the vehicle routing system 1692 is to use the routingpreference of the user 1690 and the corresponding effect on the at leastone routing parameter to govern routing of the set of vehicles toachieve a bad weather avoidance objective. In embodiments, the vehiclerouting system 1692 is to use the routing preference of the user 1690and the corresponding effect on the at least one routing parameter togovern routing of the set of vehicles to achieve a maximum travel timeobjective. In embodiments, the vehicle routing system 1692 is to use therouting preference of the user 1690 and the corresponding effect on theat least one routing parameter to govern routing of the set of vehiclesto achieve a maximum speed limit objective. In embodiments, the vehiclerouting system 1692 is to use the routing preference of the user 1690and the corresponding effect on the at least one routing parameter togovern routing of the set of vehicles to achieve an avoidance of tollroads objective. In embodiments, the vehicle routing system 1692 is touse the routing preference of the user 1690 and the corresponding effecton the at least one routing parameter to govern routing of the set ofvehicles to achieve an avoidance of city roads objective.

In embodiments, the vehicle routing system 1692 is to use the routingpreference of the user 1690 and the corresponding effect on the at leastone routing parameter to govern routing of the set of vehicles toachieve an avoidance of undivided highways objective. In embodiments,the vehicle routing system 1692 is to use the routing preference of theuser 1690 and the corresponding effect on the at least one routingparameter to govern routing of the set of vehicles to achieve anavoidance of left turns objective. In embodiments, the vehicle routingsystem 1692 is to use the routing preference of the user 1690 and thecorresponding effect on the at least one routing parameter to governrouting of the set of vehicles to achieve an avoidance ofdriver-operated vehicles objective.

FIG. 17 illustrates a method 1700 of reward-based coordinated vehiclerouting in accordance with embodiments of the systems and methodsdisclosed herein. At 1702, the method includes receiving through areward-based interface a response of a user related to a set of vehiclesto a reward offered in the reward-based interface. At 1704, the methodincludes determining a routing preference based on the response of theuser. At 1706, the method includes determining at least one user actionresulting from the response of the user to the reward. At 1708, themethod includes determining a corresponding effect of the at least oneuser action on at least one routing parameter. At 1709, the methodincludes governing routing of the set of vehicles responsive to therouting preference and the corresponding effect on the at least onerouting parameter.

In embodiments, the user 1690 is a rider of at least one vehicle 1610 inthe set of vehicles 1694. In embodiments, the user 1690 is anadministrator for a set of roadways to be used by at least one vehicle1610 in the set of vehicles 1694. In embodiments, the user 1690 is anadministrator for a fleet of vehicles including the set of vehicles1694.

In embodiments, the reward-based interface 16104 is disposed forin-vehicle use. In embodiments, the at least one routing parameter 1630includes at least one of: traffic congestion, desired arrival times,preferred routes, fuel efficiency, pollution reduction, accidentavoidance, avoiding bad weather, avoiding bad road conditions, reducedfuel consumption, reduced carbon footprint, reduced noise in a region,avoiding high-crime regions, collective satisfaction, maximum speedlimit, avoidance of toll roads, avoidance of city roads, avoidance ofundivided highways, avoidance of left turns, and avoidance ofdriver-operated vehicles. In embodiments, the user 1690 responds to thereward 16102 offered in the reward-based interface 16104 by acceptingthe reward 16102 offered in the interface, rejecting the reward 16102offered in the reward-based interface 16104, or ignoring the reward16102 offered in the reward-based interface 16104. In embodiments, theuser 1690 indicates the routing preference by either accepting orrejecting the reward 16102 offered in the reward-based interface 16104.In embodiments, the user 1690 indicates the routing preference byundertaking an action in at least one vehicle 1610 in the set ofvehicles 1694 that facilitates transferring the reward 16102 to the user1690.

In embodiments, the method further comprises sending, via a reward offerresponse processing circuit 16105, a signal to the vehicle routingsystem 1692 to select a vehicle route that permits adequate time for theuser 1690 to perform the at least one user action. In embodiments, themethod further comprises: sending, via a reward offer responseprocessing circuit 16105, a signal to a vehicle routing system 1692, thesignal indicating a destination of a vehicle associated with the atleast one user action; and adjusting, by the vehicle routing system1692, a route of the vehicle 1695 associated with the at least one useraction to include the destination. In embodiments, the reward 16102 isassociated with achieving a vehicle routing fuel efficiency objective.

In embodiments, the reward 16102 is associated with achieving a vehiclerouting reduced traffic objective. In embodiments, the reward 16102 isassociated with achieving a vehicle routing reduced pollution objective.In embodiments, the reward 16102 is associated with achieving a vehiclerouting reduced carbon footprint objective. In embodiments, the reward16102 is associated with achieving a vehicle routing reduced noise inneighborhoods objective. In embodiments, reward 16102 is associated withachieving a vehicle routing collective satisfaction objective. Inembodiments, the reward 16102 is associated with achieving a vehiclerouting avoiding accident scenes objective.

In embodiments, the reward 16102 is associated with achieving a vehiclerouting avoiding high-crime areas objective. In embodiments, the reward16102 is associated with achieving a vehicle routing reduced trafficcongestion objective. In embodiments, the reward 16102 is associatedwith achieving a vehicle routing bad weather avoidance objective. Inembodiments, the reward 16102 is associated with achieving a vehiclerouting maximum travel time objective. In embodiments, the reward 16102is associated with achieving a vehicle routing maximum speed limitobjective. In embodiments, the reward 16102 is associated with achievinga vehicle routing avoidance of toll roads objective. In embodiments, thereward 16102 is associated with achieving a vehicle routing avoidance ofcity roads objective. In embodiments, the reward 16102 is associatedwith achieving a vehicle routing avoidance of undivided highwaysobjective. In embodiments, the reward 16102 is associated with achievinga vehicle routing avoidance of left turns objective. In embodiments, thereward 16102 is associated with achieving a vehicle routing avoidance ofdriver-operated vehicles objective.

Referring to FIG. 18 , in embodiments provided herein are transportationsystems 1811 having a data processing system 1862 for taking data 18114from a plurality 1869 of social data sources 18107 and using a neuralnetwork 18108 to predict an emerging transportation need 18112 for agroup of individuals. Among the various social data sources 18107, suchas those described above, a large amount of data is available relatingto social groups, such as friend groups, families, workplace colleagues,club members, people having shared interests or affiliations, politicalgroups, and others. The expert system described above can be trained, asdescribed throughout, such as using a training data set of humanpredictions and/or a model, with feedback of outcomes, to predict thetransportation needs of a group. For example, based on a discussionthread of a social group as indicated at least in part on a socialnetwork feed, it may become evident that a group meeting or trip willtake place, and the system may (such as using location information forrespective members, as well as indicators of a set of destinations ofthe trip), predict where and when each member would need to travel inorder to participate. Based on such a prediction, the system couldautomatically identify and show options for travel, such as availablepublic transportation options, flight options, ride share options, andthe like. Such options may include ones by which the group may sharetransportation, such as indicating a route that results in picking up aset of members of the group for travel together. Social mediainformation may include posts, tweets, comments, chats, photographs, andthe like and may be processed as noted above.

An aspect provided herein includes a system 1811 for transportation,comprising: a data processing system 1862 for taking data 18114 from aplurality 1869 of social data sources 18107 and using a neural network18108 to predict an emerging transportation need 18112 for a group ofindividuals 18110.

FIG. 19 illustrates a method 1900 of predicting a common transportationneed for a group in accordance with embodiments of the systems andmethods disclosed herein. At 1902, the method includes gathering socialmedia-sourced data about a plurality of individuals, the data beingsourced from a plurality of social media sources. At 1904, the methodincludes processing the data to identify a subset of the plurality ofindividuals who form a social group based on group affiliationreferences in the data. At 1906, the method includes detecting keywordsin the data indicative of a transportation need. At 1908, the methodincludes using a neural network trained to predict transportation needsbased on the detected keywords to identify the common transportationneed for the subset of the plurality of individuals.

Referring to FIG. 18 and FIG. 19 , in embodiments, the neural network18108 is a convolutional neural network 18113. In embodiments, theneural network 18108 is trained based on a model that facilitatesmatching phrases in social media with transportation activity. Inembodiments, the neural network 18108 predicts at least one of adestination and an arrival time for the subset 18110 of the plurality ofindividuals sharing the common transportation need. In embodiments, theneural network 18108 predicts the common transportation need based onanalysis of transportation need-indicative keywords detected in adiscussion thread among a portion of individuals in the social group. Inembodiments, the method further comprises identifying at least oneshared transportation service 18111 that facilitates a portion of thesocial group meeting the predicted common transportation need 18112. Inembodiments, the at least one shared transportation service comprisesgenerating a vehicle route that facilitates picking up the portion ofthe social group.

FIG. 20 illustrates a method 2000 of predicting a group transportationneed for a group in accordance with embodiments of the systems andmethods disclosed herein. At 2002, the method includes gathering socialmedia-sourced data about a plurality of individuals, the data beingsourced from a plurality of social media sources. At 2004, the methodincludes processing the data to identify a subset of the plurality ofindividuals who share the group transportation need. At 2006, the methodincludes detecting keywords in the data indicative of the grouptransportation need for the subset of the plurality of individuals. At2008, the method includes predicting the group transportation need usinga neural network trained to predict transportation needs based on thedetected keywords. At 2009, the method includes directing a vehiclerouting system to meet the group transportation need.

Referring to FIG. 18 and FIG. 20 , in embodiments, the neural network18108 is a convolutional neural network 18113. In embodiments, directingthe vehicle routing system to meet the group transportation needinvolves routing a plurality of vehicles to a destination derived fromthe social media-sourced data 18114. In embodiments, the neural network18108 is trained based on a model that facilitates matching phrases inthe social media-sourced data 18114 with transportation activities. Inembodiments, the method further comprises predicting, by the neuralnetwork 18108, at least one of a destination and an arrival time for thesubset 18110 of the plurality 18109 of individuals sharing the grouptransportation need. In embodiments, the method further comprisespredicting, by the neural network 18108, the group transportation needbased on an analysis of transportation need-indicative keywords detectedin a discussion thread in the social media-sourced data 18114. Inembodiments, the method further comprises identifying at least oneshared transportation service 18111 that facilitates meeting thepredicted group transportation need for at least a portion of the subset18110 of the plurality of individuals. In embodiments, the at least oneshared transportation service 18111 comprises generating a vehicle routethat facilitates picking up the at least the portion of the subset 18110of the plurality of individuals.

FIG. 21 illustrates a method 2100 of predicting a group transportationneed in accordance with embodiments of the systems and methods disclosedherein. At 2102, the method includes gathering social media-sourced datafrom a plurality of social media sources. At 2104, the method includesprocessing the data to identify an event. At 2106, the method includesdetecting keywords in the data indicative of the event to determine atransportation need associated with the event. At 2108, the methodincludes using a neural network trained to predict transportation needsbased at least in part on social media-sourced data to direct a vehiclerouting system to meet the transportation need.

Referring to FIG. 18 and FIG. 21 , in embodiments, the neural network18108 is a convolutional neural network 18113. In embodiments, thevehicle routing system is directed to meet the transportation need byrouting a plurality of vehicles to a location associated with the event.In embodiments, the vehicle routing system is directed to meet thetransportation need by routing a plurality of vehicles to avoid a regionproximal to a location associated with the event. In embodiments, thevehicle routing system is directed to meet the transportation need byrouting vehicles associated with users whose social media-sourced data18114 do not indicate the transportation need to avoid a region proximalto a location associated with the event. In embodiments, the methodfurther comprises presenting at least one transportation service forsatisfying the transportation need. In embodiments, the neural network18108 is trained based on a model that facilitates matching phrases insocial media-sourced data 18114 with transportation activity.

In embodiments, the neural network 18108 predicts at least one of adestination and an arrival time for individuals attending the event. Inembodiments, the neural network 18108 predicts the transportation needbased on analysis of transportation need-indicative keywords detected ina discussion thread in the social media-sourced data 18114. Inembodiments, the method further comprises identifying at least oneshared transportation service that facilitates meeting the predictedtransportation need for at least a subset of individuals identified inthe social media-sourced data 18114. In embodiments, the at least oneshared transportation service comprises generating a vehicle route thatfacilitates picking up the portion of the subset of individualsidentified in the social media-sourced data 18114.

Referring to FIG. 22 , in embodiments provided herein are transportationsystems 2211 having a data processing system 2211 for taking socialmedia data 22114 from a plurality 2269 of social data sources 22107 andusing a hybrid neural network 2247 to optimize an operating state of atransportation system 22111 based on processing the social data sources22107 with the hybrid neural network 2247. A hybrid neural network 2247may have, for example, a neural network component that makes aclassification or prediction based on processing social media data 22114(such as predicting a high level of attendance of an event by processingimages on many social media feeds that indicate interest in the event bymany people, prediction of traffic, classification of interest by anindividual in a topic, and many others) and another component thatoptimizes an operating state of a transportation system, such as anin-vehicle state, a routing state (for an individual vehicle 2210 or aset of vehicles 2294), a user-experience state, or other state describedthroughout this disclosure (e.g., routing an individual early to a venuelike a music festival where there is likely to be very high attendance,playing music content in a vehicle 2210 for bands who will be at themusic festival, or the like).

An aspect provided herein includes a system for transportation,comprising: a data processing system 2211 for taking social media data22114 from a plurality 2269 of social data sources 22107 and using ahybrid neural network 2247 to optimize an operating state of atransportation system based on processing the data 22114 from theplurality 2269 of social data sources 22107 with the hybrid neuralnetwork 2247.

An aspect provided herein includes a hybrid neural network system 22115for transportation system optimization, the hybrid neural network system22115 comprising a hybrid neural network 2247, including: a first neuralnetwork 2222 that predicts a localized effect 22116 on a transportationsystem through analysis of social medial data 22114 sourced from aplurality 2269 of social media data sources 22107; and a second neuralnetwork 2220 that optimizes an operating state of the transportationsystem based on the predicted localized effect 22116.

In embodiments, at least one of the first neural network 2222 and thesecond neural network 2220 is a convolutional neural network. Inembodiments, the second neural network 2220 is to optimize an in-vehiclerider experience state. In embodiments, the first neural network 2222identifies a set of vehicles 2294 contributing to the localized effect22116 based on correlation of vehicle location and an area of thelocalized effect 22116. In embodiments, the second neural network 2220is to optimize a routing state of the transportation system for vehiclesproximal to a location of the localized effect 22116. In embodiments,the hybrid neural network 2247 is trained for at least one of thepredicting and optimizing based on keywords in the social media dataindicative of an outcome of a transportation system optimization action.In embodiments, the hybrid neural network 2247 is trained for at leastone of predicting and optimizing based on social media posts.

In embodiments, the hybrid neural network 2247 is trained for at leastone of predicting and optimizing based on social media feeds. Inembodiments, the hybrid neural network 2247 is trained for at least oneof predicting and optimizing based on ratings derived from the socialmedia data 22114. In embodiments, the hybrid neural network 2247 istrained for at least one of predicting and optimizing based on like ordislike activity detected in the social media data 22114. Inembodiments, the hybrid neural network 2247 is trained for at least oneof predicting and optimizing based on indications of relationships inthe social media data 22114. In embodiments, the hybrid neural network2247 is trained for at least one of predicting and optimizing based onuser behavior detected in the social media data 22114. In embodiments,the hybrid neural network 2247 is trained for at least one of predictingand optimizing based on discussion threads in the social media data22114.

In embodiments, the hybrid neural network 2247 is trained for at leastone of predicting and optimizing based on chats in the social media data22114. In embodiments, the hybrid neural network 2247 is trained for atleast one of predicting and optimizing based on photographs in thesocial media data 22114. In embodiments, the hybrid neural network 2247is trained for at least one of predicting and optimizing based ontraffic-affecting information in the social media data 22114. Inembodiments, the hybrid neural network 2247 is trained for at least oneof predicting and optimizing based on an indication of a specificindividual at a location in the social media data 22114. In embodiments,the specific individual is a celebrity. In embodiments, the hybridneural network 2247 is trained for at least one of predicting andoptimizing based a presence of a rare or transient phenomena at alocation in the social media data 22114.

In embodiments, the hybrid neural network 2247 is trained for at leastone of predicting and optimizing based a commerce-related event at alocation in the social media data 22114. In embodiments, the hybridneural network 2247 is trained for at least one of predicting andoptimizing based an entertainment event at a location in the socialmedia data 22114. In embodiments, the social media data analyzed topredict a localized effect on a transportation system includes trafficconditions. In embodiments, the social media data analyzed to predict alocalized effect on a transportation system includes weather conditions.In embodiments, the social media data analyzed to predict a localizedeffect on a transportation system includes entertainment options.

In embodiments, the social media data analyzed to predict a localizedeffect on a transportation system includes risk-related conditions. Inembodiments, the risk-related conditions include crowds gathering forpotentially dangerous reasons. In embodiments, the social media dataanalyzed to predict a localized effect on a transportation systemincludes commerce-related conditions. In embodiments, the social mediadata analyzed to predict a localized effect on a transportation systemincludes goal-related conditions.

In embodiments, the social media data analyzed to predict a localizedeffect on a transportation system includes estimates of attendance at anevent. In embodiments, the social media data analyzed to predict alocalized effect on a transportation system includes predictions ofattendance at an event. In embodiments, the social media data analyzedto predict a localized effect on a transportation system includes modesof transportation. In embodiments, the modes of transportation includecar traffic. In embodiments, the modes of transportation include publictransportation options.

In embodiments, the social media data analyzed to predict a localizedeffect on a transportation system includes hash tags. In embodiments,the social media data analyzed to predict a localized effect on atransportation system includes trending of topics. In embodiments, anoutcome of a transportation system optimization action is reducing fuelconsumption. In embodiments, an outcome of a transportation systemoptimization action is reducing traffic congestion. In embodiments, anoutcome of a transportation system optimization action is reducedpollution. In embodiments, an outcome of a transportation systemoptimization action is bad weather avoidance. In embodiments, anoperating state of the transportation system being optimized includes anin-vehicle state. In embodiments, an operating state of thetransportation system being optimized includes a routing state.

In embodiments, the routing state is for an individual vehicle 2210. Inembodiments, the routing state is for a set of vehicles 2294. Inembodiments, an operating state of the transportation system beingoptimized includes a user-experience state.

FIG. 23 illustrates a method 2300 of optimizing an operating state of atransportation system in accordance with embodiments of the systems andmethods disclosed herein. At 2302 the method includes gathering socialmedia-sourced data about a plurality of individuals, the data beingsourced from a plurality of social media sources. At 2304 the methodincludes optimizing, using a hybrid neural network, the operating stateof the transportation system. At 2306 the method includes predicting, bya first neural network of the hybrid neural network, an effect on thetransportation system through an analysis of the social media-sourceddata. At 2308 the method includes optimizing, by a second neural networkof the hybrid neural network, at least one operating state of thetransportation system responsive to the predicted effect thereon.

Referring to FIG. 22 and FIG. 23 , in embodiments, at least one of thefirst neural network 2222 and the second neural network 2220 is aconvolutional neural network. In embodiments, the second neural network2220 optimizes an in-vehicle rider experience state. In embodiments, thefirst neural network 2222 identifies a set of vehicles contributing tothe effect based on correlation of vehicle location and an effect area.In embodiments, the second neural network 2220 optimizes a routing stateof the transportation system for vehicles proximal to a location of theeffect.

In embodiments, the hybrid neural network 2247 is trained for at leastone of the predicting and optimizing based on keywords in the socialmedia data indicative of an outcome of a transportation systemoptimization action. In embodiments, the hybrid neural network 2247 istrained for at least one of predicting and optimizing based on socialmedia posts. In embodiments, the hybrid neural network 2247 is trainedfor at least one of predicting and optimizing based on social mediafeeds. In embodiments, the hybrid neural network 2247 is trained for atleast one of predicting and optimizing based on ratings derived from thesocial media data 22114. In embodiments, the hybrid neural network 2247is trained for at least one of predicting and optimizing based on likeor dislike activity detected in the social media data 22114. Inembodiments, the hybrid neural network 2247 is trained for at least oneof predicting and optimizing based on indications of relationships inthe social media data 22114.

In embodiments, the hybrid neural network 2247 is trained for at leastone of predicting and optimizing based on user behavior detected in thesocial media data 22114. In embodiments, the hybrid neural network 2247is trained for at least one of predicting and optimizing based ondiscussion threads in the social media data 22114. In embodiments, thehybrid neural network 2247 is trained for at least one of predicting andoptimizing based on chats in the social media data 22114. Inembodiments, the hybrid neural network 2247 is trained for at least oneof predicting and optimizing based on photographs in the social mediadata 22114. In embodiments, the hybrid neural network 2247 is trainedfor at least one of predicting and optimizing based on traffic-affectinginformation in the social media data 22114.

In embodiments, the hybrid neural network 2247 is trained for at leastone of predicting and optimizing based on an indication of a specificindividual at a location in the social media data. In embodiments, thespecific individual is a celebrity. In embodiments, the hybrid neuralnetwork 2247 is trained for at least one of predicting and optimizingbased a presence of a rare or transient phenomena at a location in thesocial media data. In embodiments, the hybrid neural network 2247 istrained for at least one of predicting and optimizing based acommerce-related event at a location in the social media data. Inembodiments, the hybrid neural network 2247 is trained for at least oneof predicting and optimizing based an entertainment event at a locationin the social media data. In embodiments, the social media data analyzedto predict an effect on a transportation system includes trafficconditions.

In embodiments, the social media data analyzed to predict an effect on atransportation system includes weather conditions. In embodiments, thesocial media data analyzed to predict an effect on a transportationsystem includes entertainment options. In embodiments, the social mediadata analyzed to predict an effect on a transportation system includesrisk-related conditions. In embodiments, the risk-related conditionsinclude crowds gathering for potentially dangerous reasons. Inembodiments, the social media data analyzed to predict an effect on atransportation system includes commerce-related conditions. Inembodiments, the social media data analyzed to predict an effect on atransportation system includes goal-related conditions.

In embodiments, the social media data analyzed to predict an effect on atransportation system includes estimates of attendance at an event. Inembodiments, the social media data analyzed to predict an effect on atransportation system includes predictions of attendance at an event. Inembodiments, the social media data analyzed to predict an effect on atransportation system includes modes of transportation. In embodiments,the modes of transportation include car traffic. In embodiments, themodes of transportation include public transportation options. Inembodiments, the social media data analyzed to predict an effect on atransportation system includes hash tags. In embodiments, the socialmedia data analyzed to predict an effect on a transportation systemincludes trending of topics.

In embodiments, an outcome of a transportation system optimizationaction is reducing fuel consumption. In embodiments, an outcome of atransportation system optimization action is reducing trafficcongestion. In embodiments, an outcome of a transportation systemoptimization action is reduced pollution. In embodiments, an outcome ofa transportation system optimization action is bad weather avoidance. Inembodiments, the operating state of the transportation system beingoptimized includes an in-vehicle state. In embodiments, the operatingstate of the transportation system being optimized includes a routingstate. In embodiments, the routing state is for an individual vehicle.In embodiments, the routing state is for a set of vehicles. Inembodiments, the operating state of the transportation system beingoptimized includes a user-experience state.

FIG. 24 illustrates a method 2400 of optimizing an operating state of atransportation system in accordance with embodiments of the systems andmethods disclosed herein. At 2402 the method includes using a firstneural network of a hybrid neural network to classify social media datasourced from a plurality of social media sources as affecting atransportation system. At 2404 the method includes using a secondnetwork of the hybrid neural network to predict at least one operatingobjective of the transportation system based on the classified socialmedia data. At 2406 the method includes using a third network of thehybrid neural network to optimize the operating state of thetransportation system to achieve the at least one operating objective ofthe transportation system.

Referring to FIG. 22 and FIG. 24 , in embodiments, at least one of theneural networks in the hybrid neural network 2247 is a convolutionalneural network.

Referring to FIG. 25 , in embodiments provided herein are transportationsystems 2511 having a data processing system 2562 for taking socialmedia data 25114 from a plurality of social data sources 25107 and usinga hybrid neural network 2547 to optimize an operating state 2545 of avehicle 2510 based on processing the social data sources with the hybridneural network 2547. In embodiments, the hybrid neural network 2547 caninclude one neural network category for prediction, another forclassification, and another for optimization of one or more operatingstates, such as based on optimizing one or more desired outcomes (such aproviding efficient travel, highly satisfying rider experiences,comfortable rides, on-time arrival, or the like). Social data sources2569 may be used by distinct neural network categories (such as any ofthe types described herein) to predict travel times, to classify contentsuch as for profiling interests of a user, to predict objectives for atransportation plan (such as what will provide overall satisfaction foran individual or a group) and the like. Social data sources 2569 mayalso inform optimization, such as by providing indications of successfuloutcomes (e.g., a social data source 25107 like a Facebook feed mightindicate that a trip was “amazing” or “horrible,” a Yelp review mightindicate a restaurant was terrible, or the like). Thus, social datasources 2569, by contributing to outcome tracking, can be used to traina system to optimize transportation plans, such as relating to timing,destinations, trip purposes, what individuals should be invited, whatentertainment options should be selected, and many others.

An aspect provided herein includes a system for transportation 2511,comprising: a data processing system 2562 for taking social media data25114 from a plurality of social data sources 25107 and using a hybridneural network 2547 to optimize an operating state 2545 of a vehicle2510 based on processing the data 25114 from the plurality of socialdata sources 25107 with the hybrid neural network 2547.

FIG. 26 illustrates a method 2600 of optimizing an operating state of avehicle in accordance with embodiments of the systems and methodsdisclosed herein. At 2602 the method includes classifying, using a firstneural network 2522 (FIG. 25 ) of a hybrid neural network, social mediadata 25119 (FIG. 25 ) sourced from a plurality of social media sourcesas affecting a transportation system. At 2604 the method includespredicting, using a second neural network 2520 (FIG. 25 ) of the hybridneural network, one or more effects 25118 (FIG. 25 ) of the classifiedsocial media data on the transportation system. At 2606 the methodincludes optimizing, using a third neural network 25117 (FIG. 25 ) ofthe hybrid neural network, a state of at least one vehicle of thetransportation system, wherein the optimizing addresses an influence ofthe predicted one or more effects on the at least one vehicle.

Referring to FIG. 25 and FIG. 26 , in embodiments, at least one of theneural networks in the hybrid neural network 2547 is a convolutionalneural network. In embodiments, the social media data 25114 includessocial media posts. In embodiments, the social media data 25114 includessocial media feeds. In embodiments, the social media data 25114 includeslike or dislike activity detected in the social media. In embodiments,the social media data 25114 includes indications of relationships. Inembodiments, the social media data 25114 includes user behavior. Inembodiments, the social media data 25114 includes discussion threads. Inembodiments, the social media data 25114 includes chats. In embodiments,the social media data 25114 includes photographs.

In embodiments, the social media data 25114 includes traffic-affectinginformation. In embodiments, the social media data 25114 includes anindication of a specific individual at a location. In embodiments, thesocial media data 25114 includes an indication of a celebrity at alocation. In embodiments, the social media data 25114 includes presenceof a rare or transient phenomena at a location. In embodiments, thesocial media data 25114 includes a commerce-related event. Inembodiments, the social media data 25114 includes an entertainment eventat a location. In embodiments, the social media data 25114 includestraffic conditions. In embodiments, the social media data 25114 includesweather conditions. In embodiments, the social media data 25114 includesentertainment options.

In embodiments, the social media data 25114 includes risk-relatedconditions. In embodiments, the social media data 25114 includespredictions of attendance at an event. In embodiments, the social mediadata 25114 includes estimates of attendance at an event. In embodiments,the social media data 25114 includes modes of transportation used withan event. In embodiments, the effect 25118 on the transportation systemincludes reducing fuel consumption. In embodiments, the effect 25118 onthe transportation system includes reducing traffic congestion. Inembodiments, the effect 25118 on the transportation system includesreduced carbon footprint. In embodiments, the effect 25118 on thetransportation system includes reduced pollution.

In embodiments, the optimized state 2544 of the at least one vehicle2510 is an operating state of the vehicle 2545. In embodiments, theoptimized state of the at least one vehicle includes an in-vehiclestate. In embodiments, the optimized state of the at least one vehicleincludes a rider state. In embodiments, the optimized state of the atleast one vehicle includes a routing state. In embodiments, theoptimized state of the at least one vehicle includes user experiencestate. In embodiments, a characterization of an outcome of theoptimizing in the social media data 25114 is used as feedback to improvethe optimizing. In embodiments, the feedback includes likes and dislikesof the outcome. In embodiments, the feedback includes social medialactivity referencing the outcome.

In embodiments, the feedback includes trending of social media activityreferencing the outcome. In embodiments, the feedback includes hash tagsassociated with the outcome. In embodiments, the feedback includesratings of the outcome. In embodiments, the feedback includes requestsfor the outcome.

FIG. 26A illustrates a method 26A00 of optimizing an operating state ofa vehicle in accordance with embodiments of the systems and methodsdisclosed herein. At 26A02 the method includes classifying, using afirst neural network of a hybrid neural network, social media datasourced from a plurality of social media sources as affecting atransportation system. At 26A04 the method includes predicting, using asecond neural network of the hybrid neural network, at least onevehicle-operating objective of the transportation system based on theclassified social media data. At 26A06 the method includes optimizing,using a third neural network of the hybrid neural network, a state of avehicle in the transportation system to achieve the at least onevehicle-operating objective of the transportation system.

Referring to FIG. 25 and FIG. 26A, in embodiments, at least one of theneural networks in the hybrid neural network 2547 is a convolutionalneural network. In embodiments, the vehicle-operating objectivecomprises achieving a rider state of at least one rider in the vehicle.In embodiments, the social media data 25114 includes social media posts.

In embodiments, the social media data 25114 includes social media feeds.In embodiments, the social media data 25114 includes like and dislikeactivity detected in the social media. In embodiments, the social mediadata 25114 includes indications of relationships. In embodiments, thesocial media data 25114 includes user behavior. In embodiments, thesocial media data 25114 includes discussion threads. In embodiments, thesocial media data 25114 includes chats. In embodiments, the social mediadata 25114 includes photographs. In embodiments, the social media data25114 includes traffic-affecting information.

In embodiments, the social media data 25114 includes an indication of aspecific individual at a location. In embodiments, the social media data25114 includes an indication of a celebrity at a location. Inembodiments, the social media data 25114 includes presence of a rare ortransient phenomena at a location. In embodiments, the social media data25114 includes a commerce-related event. In embodiments, the socialmedia data 25114 includes an entertainment event at a location. Inembodiments, the social media data 25114 includes traffic conditions. Inembodiments, the social media data 25114 includes weather conditions. Inembodiments, the social media data 25114 includes entertainment options.

In embodiments, the social media data 25114 includes risk-relatedconditions. In embodiments, the social media data 25114 includespredictions of attendance at an event. In embodiments, the social mediadata 25114 includes estimates of attendance at an event. In embodiments,the social media data 25114 includes modes of transportation used withan event. In embodiments, the effect on the transportation systemincludes reducing fuel consumption. In embodiments, the effect on thetransportation system includes reducing traffic congestion. Inembodiments, the effect on the transportation system includes reducedcarbon footprint. In embodiments, the effect on the transportationsystem includes reduced pollution. In embodiments, the optimized stateof the vehicle is an operating state of the vehicle.

In embodiments, the optimized state of the vehicle includes anin-vehicle state. In embodiments, the optimized state of the vehicleincludes a rider state. In embodiments, the optimized state of thevehicle includes a routing state. In embodiments, the optimized state ofthe vehicle includes user experience state. In embodiments, acharacterization of an outcome of the optimizing in the social mediadata is used as feedback to improve the optimizing. In embodiments, thefeedback includes likes or dislikes of the outcome. In embodiments, thefeedback includes social medial activity referencing the outcome. Inembodiments, the feedback includes trending of social media activityreferencing the outcome.

In embodiments, the feedback includes hash tags associated with theoutcome. In embodiments, the feedback includes ratings of the outcome.In embodiments, the feedback includes requests for the outcome.

Referring to FIG. 27 , in embodiments provided herein are transportationsystems 2711 having a data processing system 2762 for taking data 27114from a plurality 2769 of social data sources 27107 and using a hybridneural network 2747 to optimize satisfaction 27121 of at least one rider27120 in a vehicle 2710 based on processing the social data sources withthe hybrid neural network 2747. Social data sources 2769 may be used,for example, to predict what entertainment options are most likely to beeffective for a rider 27120 by one neural network category, whileanother neural network category may be used to optimize a routing plan(such as based on social data that indicates likely traffic, points ofinterest, or the like). Social data 27114 may also be used for outcometracking and feedback to optimize the system, both as to entertainmentoptions and as to transportation planning, routing, or the like.

An aspect provided herein includes a system for transportation 2711,comprising: a data processing system 2762 for taking data 27114 from aplurality 2769 of social data sources 27107 and using a hybrid neuralnetwork 2747 to optimize satisfaction 27121 of at least one rider 27120in a vehicle 2710 based on processing the data 27114 from the plurality2769 of social data sources 27107 with the hybrid neural network 2747.

FIG. 28 illustrates a method 2800 of optimizing rider satisfaction inaccordance with embodiments of the systems and methods disclosed herein.At 2802 the method includes classifying, using a first neural network2722 (FIG. 27 ) of a hybrid neural network, social media data 27119(FIG. 27 ) sourced from a plurality of social media sources asindicative of an effect on a transportation system. At 2804 the methodincludes predicting, using a second neural network 2720 (FIG. 27 ) ofthe hybrid neural network, at least one aspect 27122 (FIG. 27 ) of ridersatisfaction affected by an effect on the transportation system derivedfrom the social media data classified as indicative of an effect on thetransportation system. At 2806 the method includes optimizing, using athird neural network 27117 (FIG. 27 ) of the hybrid neural network, theat least one aspect of rider satisfaction for at least one rideroccupying a vehicle in the transportation system.

Referring to FIG. 27 and FIG. 28 , in embodiments, at least one of theneural networks in the hybrid neural network 2547 is a convolutionalneural network. In embodiments, the at least one aspect of ridersatisfaction 27121 is optimized by predicting an entertainment optionfor presenting to the rider. In embodiments, the at least one aspect ofrider satisfaction 27121 is optimized by optimizing route planning for avehicle occupied by the rider. In embodiments, the at least one aspectof rider satisfaction 27121 is a rider state and optimizing the aspectsof rider satisfaction comprising optimizing the rider state. Inembodiments, social media data specific to the rider is analyzed todetermine at least one optimizing action likely to optimize the at leastone aspect of rider satisfaction 27121. In embodiments, the optimizingaction is selected from the group of actions consisting of adjusting arouting plan to include passing points of interest to the user, avoidingtraffic congestion predicted from the social media data, and presentingentertainment options.

In embodiments, the social media data includes social media posts. Inembodiments, the social media data includes social media feeds. Inembodiments, the social media data includes like or dislike activitydetected in the social media. In embodiments, the social media dataincludes indications of relationships. In embodiments, the social mediadata includes user behavior. In embodiments, the social media dataincludes discussion threads. In embodiments, the social media dataincludes chats. In embodiments, the social media data includesphotographs.

In embodiments, the social media data includes traffic-affectinginformation. In embodiments, the social media data includes anindication of a specific individual at a location. In embodiments, thesocial media data includes an indication of a celebrity at a location.In embodiments, the social media data includes presence of a rare ortransient phenomena at a location. In embodiments, the social media dataincludes a commerce-related event. In embodiments, the social media dataincludes an entertainment event at a location. In embodiments, thesocial media data includes traffic conditions. In embodiments, thesocial media data includes weather conditions. In embodiments, thesocial media data includes entertainment options. In embodiments, thesocial media data includes risk-related conditions. In embodiments, thesocial media data includes predictions of attendance at an event. Inembodiments, the social media data includes estimates of attendance atan event. In embodiments, the social media data includes modes oftransportation used with an event. In embodiments, the effect on thetransportation system includes reducing fuel consumption. Inembodiments, the effect on the transportation system includes reducingtraffic congestion. In embodiments, the effect on the transportationsystem includes reduced carbon footprint. In embodiments, the effect onthe transportation system includes reduced pollution. In embodiments,the optimized at least one aspect of rider satisfaction is an operatingstate of the vehicle. In embodiments, the optimized at least one aspectof rider satisfaction includes an in-vehicle state. In embodiments, theoptimized at least one aspect of rider satisfaction includes a riderstate. In embodiments, the optimized at least one aspect of ridersatisfaction includes a routing state. In embodiments, the optimized atleast one aspect of rider satisfaction includes user experience state.

In embodiments, a characterization of an outcome of the optimizing inthe social media data is used as feedback to improve the optimizing. Inembodiments, the feedback includes likes or dislikes of the outcome. Inembodiments, the feedback includes social medial activity referencingthe outcome. In embodiments, the feedback includes trending of socialmedia activity referencing the outcome. In embodiments, the feedbackincludes hash tags associated with the outcome. In embodiments, thefeedback includes ratings of the outcome. In embodiments, the feedbackincludes requests for the outcome.

An aspect provided herein includes a rider satisfaction system 27123 foroptimizing rider satisfaction 27121, the system comprising: a firstneural network 2722 of a hybrid neural network 2747 to classify socialmedia data 27114 sourced from a plurality 2769 of social media sources27107 as indicative of an effect 27119 on a transportation system 2711;a second neural network 2720 of the hybrid neural network 2747 topredict at least one aspect 27122 of rider satisfaction 27121 affectedby an effect on the transportation system derived from the social mediadata classified as indicative of the effect on the transportationsystem; and a third network 27117 of the hybrid neural network 2747 tooptimize the at least one aspect of rider satisfaction 27121 for atleast one rider 2744 occupying a vehicle 2710 in the transportationsystem 2711. In embodiments, at least one of the neural networks in thehybrid neural network 2747 is a convolutional neural network.

In embodiments, the at least one aspect of rider satisfaction 27121 isoptimized by predicting an entertainment option for presenting to therider 2744. In embodiments, the at least one aspect of ridersatisfaction 27121 is optimized by optimizing route planning for avehicle 2710 occupied by the rider 2744. In embodiments, the at leastone aspect of rider satisfaction 27121 is a rider state 2737 andoptimizing the at least one aspect of rider satisfaction 27121 comprisesoptimizing the rider state 2737. In embodiments, social media dataspecific to the rider 2744 is analyzed to determine at least oneoptimizing action likely to optimize the at least one aspect of ridersatisfaction 27121. In embodiments, the at least one optimizing actionis selected from the group consisting of: adjusting a routing plan toinclude passing points of interest to the user, avoiding trafficcongestion predicted from the social media data, deriving an economicbenefit, deriving an altruistic benefit, and presenting entertainmentoptions.

In embodiments, the economic benefit is saved fuel. In embodiments, thealtruistic benefit is reduction of environmental impact. In embodiments,the social media data includes social media posts. In embodiments, thesocial media data includes social media feeds. In embodiments, thesocial media data includes like or dislike activity detected in thesocial media. In embodiments, the social media data includes indicationsof relationships. In embodiments, the social media data includes userbehavior. In embodiments, the social media data includes discussionthreads. In embodiments, the social media data includes chats. Inembodiments, the social media data includes photographs. In embodiments,the social media data includes traffic-affecting information. Inembodiments, the social media data includes an indication of a specificindividual at a location.

In embodiments, the social media data includes an indication of acelebrity at a location. In embodiments, the social media data includespresence of a rare or transient phenomena at a location. In embodiments,the social media data includes a commerce-related event. In embodiments,the social media data includes an entertainment event at a location. Inembodiments, the social media data includes traffic conditions. Inembodiments, the social media data includes weather conditions. Inembodiments, the social media data includes entertainment options. Inembodiments, the social media data includes risk-related conditions. Inembodiments, the social media data includes predictions of attendance atan event. In embodiments, the social media data includes estimates ofattendance at an event. In embodiments, the social media data includesmodes of transportation used with an event.

In embodiments, the effect on the transportation system includesreducing fuel consumption. In embodiments, the effect on thetransportation system includes reducing traffic congestion. Inembodiments, the effect on the transportation system includes reducedcarbon footprint. In embodiments, the effect on the transportationsystem includes reduced pollution. In embodiments, the optimized atleast one aspect of rider satisfaction is an operating state of thevehicle. In embodiments, the optimized at least one aspect of ridersatisfaction includes an in-vehicle state. In embodiments, the optimizedat least one aspect of rider satisfaction includes a rider state. Inembodiments, the optimized at least one aspect of rider satisfactionincludes a routing state. In embodiments, the optimized at least oneaspect of rider satisfaction includes user experience state. Inembodiments, a characterization of an outcome of the optimizing in thesocial media data is used as feedback to improve the optimizing. Inembodiments, the feedback includes likes or dislikes of the outcome. Inembodiments, the feedback includes social medial activity referencingthe outcome. In embodiments, the feedback includes trending of socialmedia activity referencing the outcome. In embodiments, the feedbackincludes hash tags associated with the outcome. In embodiments, thefeedback includes ratings of the outcome. In embodiments, the feedbackincludes requests for the outcome.

Referring to FIG. 29 , in embodiments provided herein are transportationsystems 2911 having a hybrid neural network 2947 wherein one neuralnetwork 2922 processes a sensor input 29125 about a rider 2944 of avehicle 2910 to determine an emotional state 29126 and another neuralnetwork optimizes at least one operating parameter 29124 of the vehicleto improve the rider's emotional state 2966. For example, a neural net2922 that includes one or more perceptrons 29127 that mimic human sensesmay be used to mimic or assist with determining the likely emotionalstate of a rider 29126 based on the extent to which various senses havebeen stimulated, while another neural network 2920 is used in an expertsystem that performs random and/or systematized variations of variouscombinations of operating parameters (such as entertainment settings,seat settings, suspension settings, route types and the like) withgenetic programming that promotes favorable combinations and eliminatesunfavorable ones, optionally based on input from the output of theperceptron-containing neural network 2922 that predict emotional state.These and many other such combinations are encompassed by the presentdisclosure. In FIG. 29 , perceptrons 29127 are depicted as optional.

An aspect provided herein includes a system for transportation 2911,comprising: a hybrid neural network 2947 wherein one neural network 2922processes a sensor input 29125 corresponding to a rider 2944 of avehicle 2910 to determine an emotional state 2966 of the rider 2944 andanother neural network 2920 optimizes at least one operating parameter29124 of the vehicle to improve the emotional state 2966 of the rider2944.

An aspect provided herein includes a hybrid neural network 2947 forrider satisfaction, comprising: a first neural network 2922 to detect adetected emotional state 29126 of a rider 2944 occupying a vehicle 2910through analysis of data 29125 gathered from sensors 2925 deployed in avehicle 2910 for gathering physiological conditions of the rider; and asecond neural network 2920 to optimize, for achieving a favorableemotional state of the rider, an operational parameter 29124 of thevehicle in response to the detected emotional state 29126 of the rider.

In embodiments, the first neural network 2922 is a recurrent neuralnetwork and the second neural network 2920 is a radial basis functionneural network. In embodiments, at least one of the neural networks inthe hybrid neural network 2947 is a convolutional neural network. Inembodiments, the second neural network 2920 is to optimize theoperational parameter 29124 based on a correlation between a vehicleoperating state 2945 and a rider emotional state 2966 of the rider. Inembodiments, the second neural network 2920 optimizes the operationalparameter 29124 in real time responsive to the detecting of the detectedemotional state 29126 of the rider 2944 by the first neural network2922. In embodiments, the first neural network 2922 comprises aplurality of connected nodes that form a directed cycle, the firstneural network 2922 further facilitating bi-directional flow of dataamong the connected nodes. In embodiments, the operational parameter29124 that is optimized affects at least one of: a route of the vehicle,in-vehicle audio contents, a speed of the vehicle, an acceleration ofthe vehicle, a deceleration of the vehicle, a proximity to objects alongthe route, and a proximity to other vehicles along the route.

An aspect provided herein includes an artificial intelligence system2936 for optimizing rider satisfaction, comprising: a hybrid neuralnetwork 2947, including: a recurrent neural network (e.g., in FIG. 29 ,neural network 2922 may be a recurrent neural network) to indicate achange in an emotional state of a rider 2944 in a vehicle 2910 throughrecognition of patterns of physiological data of the rider captured byat least one sensor 2925 deployed for capturing rider emotionalstate-indicative data while occupying the vehicle 2910; and a radialbasis function neural network (e.g., in FIG. 29 , neural network 2920may be a radial basis function neural network) to optimize, forachieving a favorable emotional state of the rider, an operationalparameter 29124 of the vehicle in response to the indication of changein the emotional state of the rider. In embodiments, the operationalparameter 29124 of the vehicle that is to be optimized is to bedetermined and adjusted to induce the favorable emotional state of therider.

An aspect provided herein includes an artificial intelligence system2936 for optimizing rider satisfaction, comprising: a hybrid neuralnetwork 2947, including: a convolutional neural network (in FIG. 29 ,neural network 1, depicted at reference numeral 2922, may optionally bea convolutional neural network) to indicate a change in an emotionalstate of a rider in a vehicle through recognitions of patterns of visualdata of the rider captured by at least one image sensor (in FIG. 29 ,the sensor 2925 may optionally be an image sensor) deployed forcapturing images of the rider while occupying the vehicle; and a secondneural network 2920 to optimize, for achieving a favorable emotionalstate of the rider, an operational parameter 29124 of the vehicle inresponse to the indication of change in the emotional state of therider.

In embodiments, the operational parameter 19124 of the vehicle that isto be optimized is to be determined and adjusted to induce the favorableemotional state of the rider.

Referring to FIG. 30 , in embodiments provided herein are transportationsystems 3011 having an artificial intelligence system 3036 forprocessing feature vectors of an image of a face of a rider in a vehicleto determine an emotional state and optimizing at least one operatingparameter of the vehicle to improve the rider's emotional state. A facemay be classified based on images from in-vehicle cameras, availablecellphone or other mobile device cameras, or other sources. An expertsystem, optionally trained based on a training set of data provided byhumans or trained by deep learning, may learn to adjust vehicleparameters (such as any described herein) to provide improved emotionalstates. For example, if a rider's face indicates stress, the vehicle mayselect a less stressful route, play relaxing music, play humorouscontent, or the like.

An aspect provided herein includes a transportation system 3011,comprising: an artificial intelligence system 3036 for processingfeature vectors 30130 of an image 30129 of a face 30128 of a rider 3044in a vehicle 3010 to determine an emotional state 3066 of the rider andoptimizing an operational parameter 30124 of the vehicle to improve theemotional state 3066 of the rider 3044.

In embodiments, the artificial intelligence system 3036 includes: afirst neural network 3022 to detect the emotional state 30126 of therider through recognition of patterns of the feature vectors 30130 ofthe image 30129 of the face 30128 of the rider 3044 in the vehicle 3010,the feature vectors 30130 indicating at least one of a favorableemotional state of the rider and an unfavorable emotional state of therider; and a second neural network 3020 to optimize, for achieving thefavorable emotional state of the rider, the operational parameter 30124of the vehicle in response to the detected emotional state 30126 of therider.

In embodiments, the first neural network 3022 is a recurrent neuralnetwork and the second neural network 3020 is a radial basis functionneural network. In embodiments, the second neural network 3020 optimizesthe operational parameter 30124 based on a correlation between thevehicle operating state 3045 and the emotional state 3066 of the rider.In embodiments, the second neural network 3020 is to determine anoptimum value for the operational parameter of the vehicle, and thetransportation system 3011 is to adjust the operational parameter 30124of the vehicle to the optimum value to induce the favorable emotionalstate of the rider. In embodiments, the first neural network 3022further learns to classify the patterns in the feature vectors andassociate the patterns with a set of emotional states and changesthereto by processing a training data set 30131. In embodiments, thetraining data set 30131 is sourced from at least one of a stream of datafrom an unstructured data source, a social media source, a wearabledevice, an in-vehicle sensor, a rider helmet, a rider headgear, and arider voice recognition system.

In embodiments, the second neural network 3020 optimizes the operationalparameter 30124 in real time responsive to the detecting of theemotional state of the rider by the first neural network 3022. Inembodiments, the first neural network 3022 is to detect a pattern of thefeature vectors. In embodiments, the pattern is associated with a changein the emotional state of the rider from a first emotional state to asecond emotional state. In embodiments, the second neural network 3020optimizes the operational parameter of the vehicle in response to thedetection of the pattern associated with the change in the emotionalstate. In embodiments, the first neural network 3022 comprises aplurality of interconnected nodes that form a directed cycle, the firstneural network 3022 further facilitating bi-directional flow of dataamong the interconnected nodes. In embodiments, the transportationsystem 3011 further comprises: a feature vector generation system toprocess a set of images of the face of the rider, the set of imagescaptured over an interval of time from by a plurality of image capturedevices 3027 while the rider 3044 is in the vehicle 3010, wherein theprocessing of the set of images is to produce the feature vectors 30130of the image of the face of the rider. In embodiments, thetransportation system further comprises: image capture devices 3027disposed to capture a set of images of the face of the rider in thevehicle from a plurality of perspectives; and an image processing systemto produce the feature vectors from the set of images captured from atleast one of the plurality of perspectives.

In embodiments, the transportation system 3011 further comprises aninterface 30133 between the first neural network and the imageprocessing system 30132 to communicate a time sequence of the featurevectors, wherein the feature vectors are indicative of the emotionalstate of the rider. In embodiments, the feature vectors indicate atleast one of a changing emotional state of the rider, a stable emotionalstate of the rider, a rate of change of the emotional state of therider, a direction of change of the emotional state of the rider, apolarity of a change of the emotional state of the rider; the emotionalstate of the rider is changing to the unfavorable emotional state; andthe emotional state of the rider is changing to the favorable emotionalstate.

In embodiments, the operational parameter that is optimized affects atleast one of a route of the vehicle, in-vehicle audio content, speed ofthe vehicle, acceleration of the vehicle, deceleration of the vehicle,proximity to objects along the route, and proximity to other vehiclesalong the route. In embodiments, the second neural network is tointeract with a vehicle control system to adjust the operationalparameter. In embodiments, the artificial intelligence system furthercomprises a neural network that includes one or more perceptrons thatmimic human senses that facilitates determining the emotional state ofthe rider based on an extent to which at least one of the senses of therider is stimulated. In embodiments, the artificial intelligence systemincludes: a recurrent neural network to indicate a change in theemotional state of the rider through recognition of patterns of thefeature vectors of the image of the face of the rider in the vehicle;and a radial basis function neural network to optimize, for achievingthe favorable emotional state of the rider, the operational parameter ofthe vehicle in response to the indication of the change in the emotionalstate of the rider.

In embodiments, the radial basis function neural network is to optimizethe operational parameter based on a correlation between a vehicleoperating state and a rider emotional state. In embodiments, theoperational parameter of the vehicle that is optimized is determined andadjusted to induce a favorable rider emotional state. In embodiments,the recurrent neural network further learns to classify the patterns ofthe feature vectors and associate the patterns of the feature vectors toemotional states and changes thereto from a training data set sourcedfrom at least one of a stream of data from unstructured data sources,social media sources, wearable devices, in-vehicle sensors, a riderhelmet, a rider headgear, and a rider voice system. In embodiments, theradial basis function neural network is to optimize the operationalparameter in real time responsive to the detecting of the change in theemotional state of the rider by the recurrent neural network. Inembodiments, the recurrent neural network detects a pattern of thefeature vectors that indicates the emotional state of the rider ischanging from a first emotional state to a second emotional state. Inembodiments, the radial basis function neural network is to optimize theoperational parameter of the vehicle in response to the indicated changein emotional state.

In embodiments, the recurrent neural network comprises a plurality ofconnected nodes that form a directed cycle, the recurrent neural networkfurther facilitating bi-directional flow of data among the connectednodes. In embodiments, the feature vectors indicate at least one of theemotional state of the rider is changing, the emotional state of therider is stable, a rate of change of the emotional state of the rider, adirection of change of the emotional state of the rider, and a polarityof a change of the emotional state of the rider; the emotional state ofa rider is changing to an unfavorable emotional state; and an emotionalstate of a rider is changing to a favorable emotional state. Inembodiments, the operational parameter that is optimized affects atleast one of a route of the vehicle, in-vehicle audio content, speed ofthe vehicle, acceleration of the vehicle, deceleration of the vehicle,proximity to objects along the route, and proximity to other vehiclesalong the route.

In embodiments, the radial basis function neural network is to interactwith a vehicle control system 30134 to adjust the operational parameter30124. In embodiments, the artificial intelligence system 3036 furthercomprises a neural network that includes one or more perceptrons thatmimic human senses that facilitates determining the emotional state of arider based on an extent to which at least one of the senses of therider is stimulated. In embodiments, the artificial intelligence system3036 is to maintain the favorable emotional state of the rider via amodular neural network, the modular neural network comprising: a rideremotional state determining neural network to process the featurevectors of the image of the face of the rider in the vehicle to detectpatterns. In embodiments, the patterns in the feature vectors indicateat least one of the favorable emotional state and the unfavorableemotional state; an intermediary circuit to convert data from the rideremotional state determining neural network into vehicle operationalstate data; and a vehicle operational state optimizing neural network toadjust an operational parameter of the vehicle in response to thevehicle operational state data.

In embodiments, the vehicle operational state optimizing neural networkis to adjust the operational parameter 30124 of the vehicle forachieving a favorable emotional state of the rider. In embodiments, thevehicle operational state optimizing neural network is to optimize theoperational parameter based on a correlation between a vehicle operatingstate 3045 and a rider emotional state 3066. In embodiments, theoperational parameter of the vehicle that is optimized is determined andadjusted to induce a favorable rider emotional state. In embodiments,the rider emotional state determining neural network further learns toclassify the patterns of the feature vectors and associate the patternof the feature vectors to emotional states and changes thereto from atraining data set sourced from at least one of a stream of data fromunstructured data sources, social media sources, wearable devices,in-vehicle sensors, a rider helmet, a rider headgear, and a rider voicesystem.

In embodiments, the vehicle operational state optimizing neural networkis to optimize the operational parameter 30124 in real time responsiveto the detecting of a change in an emotional state 30126 of the rider bythe rider emotional state determining neural network. In embodiments,the rider emotional state determining neural network is to detect apattern of the feature vectors 30130 that indicates the emotional stateof the rider is changing from a first emotional state to a secondemotional state. In embodiments, the vehicle operational stateoptimizing neural network is to optimize the operational parameter ofthe vehicle in response to the indicated change in emotional state. Inembodiments, the artificial intelligence system 3036 comprises aplurality of connected nodes that form a directed cycle, the artificialintelligence system further facilitating bi-directional flow of dataamong the connected nodes.

In embodiments, the feature vectors 30130 indicate at least one of theemotional state of the rider is changing, the emotional state of therider is stable, a rate of change of the emotional state of the rider, adirection of change of the emotional state of the rider, and a polarityof a change of the emotional state of the rider; the emotional state ofa rider is changing to an unfavorable emotional state; and the emotionalstate of the rider is changing to a favorable emotional state. Inembodiments, the operational parameter that is optimized affects atleast one of a route of the vehicle, in-vehicle audio content, speed ofthe vehicle, acceleration of the vehicle, deceleration of the vehicle,proximity to objects along the route, and proximity to other vehiclesalong the route. In embodiments, the vehicle operational stateoptimizing neural network interacts with a vehicle control system toadjust the operational parameter.

In embodiments, the artificial intelligence system 3036 furthercomprises a neural net that includes one or more perceptrons that mimichuman senses that facilitates determining an emotional state of a riderbased on an extent to which at least one of the senses of the rider isstimulated. It is to be understood that the terms “neural net” and“neural network” are used interchangeably in the present disclosure. Inembodiments, the rider emotional state determining neural networkcomprises one or more perceptrons that mimic human senses thatfacilitates determining an emotional state of a rider based on an extentto which at least one of the senses of the rider is stimulated. Inembodiments, the artificial intelligence system 3036 includes arecurrent neural network to indicate a change in the emotional state ofthe rider in the vehicle through recognition of patterns of the featurevectors of the image of the face of the rider in the vehicle; thetransportation system further comprising: a vehicle control system 30134to control operation of the vehicle by adjusting a plurality of vehicleoperational parameters 30124; and a feedback loop to communicate theindicated change in the emotional state of the rider between the vehiclecontrol system 30134 and the artificial intelligence system 3036. Inembodiments, the vehicle control system is to adjust at least one of theplurality of vehicle operational parameters 30124 in response to theindicated change in the emotional state of the rider. In embodiments,the vehicle controls system adjusts the at least one of the plurality ofvehicle operational parameters based on a correlation between vehicleoperational state and rider emotional state.

In embodiments, the vehicle control system adjusts the at least one ofthe plurality of vehicle operational parameters 30124 that areindicative of a favorable rider emotional state. In embodiments, thevehicle control system 30134 selects an adjustment of the at least oneof the plurality of vehicle operational parameters 30124 that isindicative of producing a favorable rider emotional state. Inembodiments, the recurrent neural network further learns to classify thepatterns of feature vectors and associate them to emotional states andchanges thereto from a training data set 30131 sourced from at least oneof a stream of data from unstructured data sources, social mediasources, wearable devices, in-vehicle sensors, a rider helmet, a riderheadgear, and a rider voice system. In embodiments, the vehicle controlsystem 30134 adjusts the at least one of the plurality of vehicleoperation parameters 30124 in real time. In embodiments, the recurrentneural network detects a pattern of the feature vectors that indicatesthe emotional state of the rider is changing from a first emotionalstate to a second emotional state. In embodiments, the vehicle operationcontrol system adjusts an operational parameter of the vehicle inresponse to the indicated change in emotional state. In embodiments, therecurrent neural network comprises a plurality of connected nodes thatform a directed cycle, the recurrent neural network further facilitatingbi-directional flow of data among the connected nodes.

In embodiments, the feature vectors indicating at least one of anemotional state of the rider is changing, an emotional state of therider is stable, a rate of change of an emotional state of the rider, adirection of change of an emotional state of the rider, and a polarityof a change of an emotional state of the rider; an emotional state of arider is changing to an unfavorable state; an emotional state of a rideris changing to a favorable state. In embodiments, the at least one ofthe plurality of vehicle operational parameters responsively adjustedaffects a route of the vehicle, in-vehicle audio content, speed of thevehicle, acceleration of the vehicle, deceleration of the vehicle,proximity to objects along the route, proximity to other vehicles alongthe route. In embodiments, the at least one of the plurality of vehicleoperation parameters that is responsively adjusted affects operation ofa powertrain of the vehicle and a suspension system of the vehicle. Inembodiments, the radial basis function neural network interacts with therecurrent neural network via an intermediary component of the artificialintelligence system 3036 that produces vehicle control data indicativeof an emotional state response of the rider to a current operationalstate of the vehicle. In embodiments, the recognition of patterns offeature vectors comprises processing the feature vectors of the image ofthe face of the rider captured during at least two of before theadjusting at least one of the plurality of vehicle operationalparameters, during the adjusting at least one of the plurality ofvehicle operational parameters, and after adjusting at least one of theplurality of vehicle operational parameters.

In embodiments, the adjusting at least one of the plurality of vehicleoperational parameters 30124 improves an emotional state of a rider in avehicle. In embodiments, the adjusting at least one of the plurality ofvehicle operational parameters causes an emotional state of the rider tochange from an unfavorable emotional state to a favorable emotionalstate. In embodiments, the change is indicated by the recurrent neuralnetwork. In embodiments, the recurrent neural network indicates a changein the emotional state of the rider responsive to a change in anoperating parameter of the vehicle by determining a difference between afirst set of feature vectors of an image of the face of a rider capturedprior to the adjusting at least one of the plurality of operatingparameters and a second set of feature vectors of an image of the faceof the rider captured during or after the adjusting at least one of theplurality of operating parameters.

In embodiments, the recurrent neural network detects a pattern of thefeature vectors that indicates an emotional state of the rider ischanging from a first emotional state to a second emotional state. Inembodiments, the vehicle operation control system adjusts an operationalparameter of the vehicle in response to the indicated change inemotional state.

Referring to FIG. 31 , in embodiments, provided herein aretransportation systems having an artificial intelligence system forprocessing a voice of a rider in a vehicle to determine an emotionalstate and optimizing at least one operating parameter of the vehicle toimprove the rider's emotional state. A voice-analysis module may takevoice input and, using a training set of labeled data where individualsindicate emotional states while speaking and/or whether others tag thedata to indicate perceived emotional states while individuals aretalking, a machine learning system (such as any of the types describedherein) may be trained (such as using supervised learning, deeplearning, or the like) to classify the emotional state of the individualbased on the voice. Machine learning may improve classification by usingfeedback from a large set of trials, where feedback in each instanceindicates whether the system has correctly assessed the emotional stateof the individual in the case of an instance of speaking. Once trainedto classify the emotional state, an expert system (optionally using adifferent machine learning system or other artificial intelligencesystem) may, based on feedback of outcomes of the emotional states of aset of individuals, be trained to optimize various vehicle parametersnoted throughout this disclosure to maintain or induce more favorablestates. For example, among many other indicators, where a voice of anindividual indicates happiness, the expert system may select orrecommend upbeat music to maintain that state. Where a voice indicatesstress, the system may recommend or provide a control signal to change aplanned route to one that is less stressful (e.g., has less stop-and-gotraffic, or that has a higher probability of an on-time arrival). Inembodiments, the system may be configured to engage in a dialog (such ason on-screen dialog or an audio dialog), such as using an intelligentagent module of the system, that is configured to use a series ofquestions to help obtain feedback from a user about the user's emotionalstate, such as asking the rider about whether the rider is experiencingstress, what the source of the stress may be (e.g., traffic conditions,potential for late arrival, behavior of other drivers, or other sourcesunrelated to the nature of the ride), what might mitigate the stress(route options, communication options (such as offering to send a notethat arrival may be delayed), entertainment options, ride configurationoptions, and the like), and the like. Driver responses may be fed asinputs to the expert system as indicators of emotional state, as well asto constrain efforts to optimize one or more vehicle parameters, such asby eliminating options for configuration that are not related to adriver's source of stress from a set of available configurations.

An aspect provided herein includes a system for transportation 3111,comprising: an artificial intelligence system 3136 for processing avoice 31135 of a rider 3144 in a vehicle 3110 to determine an emotionalstate 3166 of the rider 3144 and optimizing at least one operatingparameter 31124 of the vehicle 3110 to improve the emotional state 3166of the rider 3144.

An aspect provided herein includes an artificial intelligence system3136 for voice processing to improve rider satisfaction in atransportation system 3111, comprising: a rider voice capture system30136 deployed to capture voice output 31128 of a rider 3144 occupying avehicle 3110; a voice-analysis circuit 31132 trained using machinelearning that classifies an emotional state 31138 of the rider for thecaptured voice output of the rider; and an expert system 31139 trainedusing machine learning that optimizes at least one operating parameter31124 of the vehicle to change the rider emotional state to an emotionalstate classified as an improved emotional state.

In embodiments, the rider voice capture system 31136 comprises anintelligent agent 31140 that engages in a dialog with the rider toobtain rider feedback for use by the voice-analysis circuit 31132 forrider emotional state classification. In embodiments, the voice-analysiscircuit 31132 uses a first machine learning system and the expert system31139 uses a second machine learning system. In embodiments, the expertsystem 31139 is trained to optimize the at least one operating parameter31124 based on feedback of outcomes of the emotional states whenadjusting the at least one operating parameter 31124 for a set ofindividuals. In embodiments, the emotional state 3166 of the rider isdetermined by a combination of the captured voice output 31128 of therider and at least one other parameter. In embodiments, the at least oneother parameter is a camera-based emotional state determination of therider. In embodiments, the at least one other parameter is trafficinformation. In embodiments, the at least one other parameter is weatherinformation. In embodiments, the at least one other parameter is avehicle state. In embodiments, the at least one other parameter is atleast one pattern of physiological data of the rider. In embodiments,the at least one other parameter is a route of the vehicle. Inembodiments, the at least one other parameter is in-vehicle audiocontent. In embodiments, the at least one other parameter is a speed ofthe vehicle. In embodiments, the at least one other parameter isacceleration of the vehicle. In embodiments, the at least one otherparameter is deceleration of the vehicle. In embodiments, the at leastone other parameter is proximity to objects along the route. Inembodiments, the at least one other parameter is proximity to othervehicles along the route.

An aspect provided herein includes an artificial intelligence system3136 for voice processing to improve rider satisfaction, comprising: afirst neural network 3122 trained to classify emotional states based onanalysis of human voices detects an emotional state of a rider throughrecognition of aspects of the voice 31128 of the rider captured whilethe rider is occupying the vehicle 3110 that correlate to at least oneemotional state 3166 of the rider; and a second neural network 3120 thatoptimizes, for achieving a favorable emotional state of the rider, anoperational parameter 31124 of the vehicle in response to the detectedemotional state 31126 of the rider 3144. In embodiments, at least one ofthe neural networks is a convolutional neural network. In embodiments,the first neural network 3122 is trained through use of a training dataset that associates emotional state classes with human voice patterns.In embodiments, the first neural network 3122 is trained through the useof a training data set of voice recordings that are tagged withemotional state identifying data. In embodiments, the emotional state ofthe rider is determined by a combination of the captured voice output ofthe rider and at least one other parameter. In embodiments, the at leastone other parameter is a camera-based emotional state determination ofthe rider. In embodiments, the at least one other parameter is trafficinformation. In embodiments, the at least one other parameter is weatherinformation. In embodiments, the at least one other parameter is avehicle state.

In embodiments, the at least one other parameter is at least one patternof physiological data of the rider. In embodiments, the at least oneother parameter is a route of the vehicle. In embodiments, the at leastone other parameter is in-vehicle audio content. In embodiments, the atleast one other parameter is a speed of the vehicle. In embodiments, theat least one other parameter is acceleration of the vehicle. Inembodiments, the at least one other parameter is deceleration of thevehicle. In embodiments, the at least one other parameter is proximityto objects along the route. In embodiments, the at least one otherparameter is proximity to other vehicles along the route.

Referring now to FIG. 32 , in embodiments provided herein aretransportation systems 3211 having an artificial intelligence system3236 for processing data from an interaction of a rider with anelectronic commerce system of a vehicle to determine a rider state andoptimizing at least one operating parameter of the vehicle to improvethe rider's state. Another common activity for users of deviceinterfaces is e-commerce, such as shopping, bidding in auctions, sellingitems and the like. E-commerce systems use search functions, undertakeadvertising and engage users with various work flows that may eventuallyresult in an order, a purchase, a bid, or the like. As described hereinwith search, a set of in-vehicle-relevant search results may be providedfor e-commerce, as well as in-vehicle relevant advertising. In addition,in-vehicle-relevant interfaces and workflows may be configured based ondetection of an in-vehicle rider, which may be quite different thanworkflows that are provided for e-commerce interfaces that areconfigured for smart phones or for desktop systems. Among other factors,an in-vehicle system may have access to information that is unavailableto conventional e-commerce systems, including route information(including direction, planned stops, planned duration and the like),rider mood and behavior information (such as from past routes, as wellas detected from in-vehicle sensor sets), vehicle configuration andstate information (such as make and model), and any of the othervehicle-related parameters described throughout this disclosure. As oneexample, a rider who is bored (as detected by an in-vehicle sensor set,such as using an expert system that is trained to detect boredom) and ison a long trip (as indicated by a route that is being undertaken by acar) may be far more patient, and likely to engage in deeper, richercontent, and longer workflows, than a typical mobile user. As anotherexample, an in-vehicle rider may be far more likely to engage in freetrials, surveys, or other behaviors that promote brand engagement. Also,an in-vehicle user may be motivated to use otherwise down time toaccomplish specific goals, such as shopping for needed items. Presentingthe same interfaces, content, and workflows to in-vehicle users may missexcellent opportunities for deeper engagement that would be highlyunlikely in other settings where many more things may compete for auser's attention. In embodiments, an e-commerce system interface may beprovided for in-vehicle users, where at least one of interface displays,content, search results, advertising, and one or more associatedworkflows (such as for shopping, bidding, searching, purchasing,providing feedback, viewing products, entering ratings or reviews, orthe like) is configured based on the detection of the use of anin-vehicle interface. Displays and interactions may be furtherconfigured (optionally based on a set of rules or based on machinelearning), such as based on detection of display types (e.g., allowingricher or larger images for large, HD displays), network capabilities(e.g., enabling faster loading and lower latency by cachinglow-resolution images that initially render), audio system capabilities(such as using audio for dialog management and intelligence assistantinteractions) and the like for the vehicle. Display elements, content,and workflows may be configured by machine learning, such as by A/Btesting and/or using genetic programming techniques, such as configuringalternative interaction types and tracking outcomes. Outcomes used totrain automatic configuration of workflows for in-vehicle e-commerceinterfaces may include extent of engagement, yield, purchases, ridersatisfaction, ratings, and others. In-vehicle users may be profiled andclustered, such as by behavioral profiling, demographic profiling,psychographic profiling, location-based profiling, collaborativefiltering, similarity-based clustering, or the like, as withconventional e-commerce, but profiles may be enhanced with routeinformation, vehicle information, vehicle configuration information,vehicle state information, rider information and the like. A set ofin-vehicle user profiles, groups and clusters may be maintainedseparately from conventional user profiles, such that learning on whatcontent to present, and how to present it, is accomplished withincreased likelihood that the differences in in-vehicle shopping areaaccounted for when targeting search results, advertisements, productoffers, discounts, and the like.

An aspect provided herein includes a system for transportation 3211,comprising: an artificial intelligence system 3236 for processing datafrom an interaction of a rider 3244 with an electronic commerce systemof a vehicle to determine a rider state and optimizing at least oneoperating parameter of the vehicle to improve the rider state.

An aspect provided herein includes a rider satisfaction system 32123 foroptimizing rider satisfaction 32121, the rider satisfaction systemcomprising: an electronic commerce interface 32141 deployed for accessby a rider in a vehicle 3210; a rider interaction circuit that capturesrider interactions with the deployed interface 32141; a rider statedetermination circuit 32143 that processes the captured riderinteractions 32144 to determine a rider state 32145; and an artificialintelligence system 3236 trained to optimize, responsive to a riderstate 3237, at least one parameter 32124 affecting operation of thevehicle to improve the rider state 3237. In embodiments, the vehicle3210 comprises a system for automating at least one control parameter ofthe vehicle. In embodiments, the vehicle is at least a semi-autonomousvehicle. In embodiments, the vehicle is automatically routed. Inembodiments, the vehicle is a self-driving vehicle. In embodiments, theelectronic commerce interface is self-adaptive and responsive to atleast one of an identity of the rider, a route of the vehicle, a ridermood, rider behavior, vehicle configuration, and vehicle state.

In embodiments, the electronic commerce interface 32141 providesin-vehicle-relevant content 32146 that is based on at least one of anidentity of the rider, a route of the vehicle, a rider mood, riderbehavior, vehicle configuration, and vehicle state. In embodiments, theelectronic commerce interface executes a user interaction workflow 32147adapted for use by a rider 3244 in a vehicle 3210. In embodiments, theelectronic commerce interface provides one or more results of a searchquery 32148 that are adapted for presentation in a vehicle. Inembodiments, the search query results adapted for presentation in avehicle are presented in the electronic commerce interface along withadvertising adapted for presentation in a vehicle. In embodiments, therider interaction circuit 32142 captures rider interactions 32144 withthe interface responsive to content 32146 presented in the interface.

FIG. 33 illustrates a method 3300 for optimizing a parameter of avehicle in accordance with embodiments of the systems and methodsdisclosed herein. At 3302 the method includes capturing riderinteractions with an in-vehicle electronic commerce system. At 3304 themethod includes determining a rider state based on the captured riderinteractions and a least one operating parameter of the vehicle. At 3306the method includes processing the rider state with a rider satisfactionmodel that is adapted to suggest at least one operating parameter of avehicle the influences the rider state. At 3308 the method includesoptimizing the suggested at least one operating parameter for at leastone of maintaining and improving a rider state.

Referring to FIG. 32 and FIG. 33 , an aspect provided herein includes anartificial intelligence system 3236 for improving rider satisfaction,comprising: a first neural network 3222 trained to classify rider statesbased on analysis of rider interactions 32144 with an in-vehicleelectronic commerce system to detect a rider state 32149 throughrecognition of aspects of the rider interactions 32144 captured whilethe rider is occupying the vehicle that correlate to at least one state3237 of the rider; and a second neural network 3220 that optimizes, forachieving a favorable state of the rider, an operational parameter ofthe vehicle in response to the detected state of the rider.

Referring to FIG. 34 , in embodiments provided herein are transportationsystems 3411 having an artificial intelligence system 3436 forprocessing data from at least one Internet of Things (IoT) device 34150in the environment 34151 of a vehicle 3410 to determine a state 34152 ofthe vehicle and optimizing at least one operating parameter 34124 of thevehicle to improve a rider's state 3437 based on the determined state34152 of the vehicle.

An aspect provided herein includes a system for transportation 3411,comprising: an artificial intelligence system 3436 for processing datafrom at least one Internet of Things device 34150 in an environment34151 of a vehicle 3410 to determine a determined state 34152 of thevehicle and optimizing at least one operating parameter 34124 of thevehicle to improve a state 3437 of the rider based on the determinedstate 34152 of the vehicle 3410.

FIG. 35 illustrates a method 3500 for improving a state of a riderthrough optimization of operation of a vehicle in accordance withembodiments of the systems and methods disclosed herein. At 3502 themethod includes capturing vehicle operation-related data with at leastone Internet-of-things device. At 3504 the method includes analyzing thecaptured data with a first neural network that determines a state of thevehicle based at least in part on a portion of the captured vehicleoperation-related data. At 3506 the method includes receiving datadescriptive of a state of a rider occupying the operating vehicle. At3508 the method includes using a neural network to determine at leastone vehicle operating parameter that affects a state of a rideroccupying the operating vehicle. At 3509 the method includes using anartificial intelligence-based system to optimize the at least onevehicle operating parameter so that a result of the optimizing comprisesan improvement in the state of the rider.

Referring to FIG. 34 and FIG. 35 , in embodiments, the vehicle 3410comprises a system for automating at least one control parameter 34153of the vehicle 3410. In embodiments, the vehicle 3410 is at least asemi-autonomous vehicle. In embodiments, the vehicle 3410 isautomatically routed. In embodiments, the vehicle 3410 is a self-drivingvehicle. In embodiments, the at least one Internet-of-things device34150 is disposed in an operating environment 34154 of the vehicle. Inembodiments, the at least one Internet-of-things device 34150 thatcaptures the data about the vehicle 3410 is disposed external to thevehicle 3410. In embodiments, the at least one Internet-of-things deviceis a dashboard camera. In embodiments, the at least oneInternet-of-things device is a mirror camera. In embodiments, the atleast one Internet-of-things device is a motion sensor. In embodiments,the at least one Internet-of-things device is a seat-based sensorsystem. In embodiments, the at least one Internet-of-things device is anIoT enabled lighting system. In embodiments, the lighting system is avehicle interior lighting system. In embodiments, the lighting system isa headlight lighting system. In embodiments, the at least oneInternet-of-things device is a traffic light camera or sensor. Inembodiments, the at least one Internet-of-things device is a roadwaycamera. In embodiments, the roadway camera is disposed on at least oneof a telephone phone and a light pole. In embodiments, the at least oneInternet-of-things device is an in-road sensor. In embodiments, the atleast one Internet-of-things device is an in-vehicle thermostat. Inembodiments, the at least one Internet-of-things device is a toll booth.In embodiments, the at least one Internet-of-things device is a streetsign. In embodiments, the at least one Internet-of-things device is atraffic control light. In embodiments, the at least oneInternet-of-things device is a vehicle mounted sensor. In embodiments,the at least one Internet-of-things device is a refueling system. Inembodiments, the at least one Internet-of-things device is a rechargingsystem. In embodiments, the at least one Internet-of-things device is awireless charging station.

An aspect provided herein includes a rider state modification system34155 for improving a state 3437 of a rider 3444 in a vehicle 3410, thesystem comprising: a first neural network 3422 that operates to classifya state of the vehicle through analysis of information about the vehiclecaptured by an Internet-of-things device 34150 during operation of thevehicle 3410; and a second neural network 3420 that operates to optimizeat least one operating parameter 34124 of the vehicle based on theclassified state 34152 of the vehicle, information about a state of arider occupying the vehicle, and information that correlates vehicleoperation with an effect on rider state.

In embodiments, the vehicle comprises a system for automating at leastone control parameter 34153 of the vehicle 3410. In embodiments, thevehicle 3410 is at least a semi-autonomous vehicle. In embodiments, thevehicle 3410 is automatically routed. In embodiments, the vehicle 3410is a self-driving vehicle. In embodiments, the at least oneInternet-of-things device 34150 is disposed in an operating environmentof the vehicle 3410. In embodiments, the at least one Internet-of-thingsdevice 34150 that captures the data about the vehicle 3410 is disposedexternal to the vehicle 3410. In embodiments, the at least oneInternet-of-things device is a dashboard camera. In embodiments, the atleast one Internet-of-things device is a mirror camera. In embodiments,the at least one Internet-of-things device is a motion sensor. Inembodiments, the at least one Internet-of-things device is a seat-basedsensor system. In embodiments, the at least one Internet-of-thingsdevice is an IoT enabled lighting system.

In embodiments, the lighting system is a vehicle interior lightingsystem. In embodiments, the lighting system is a headlight lightingsystem. In embodiments, the at least one Internet-of-things device is atraffic light camera or sensor. In embodiments, the at least oneInternet-of-things device is a roadway camera. In embodiments, theroadway camera is disposed on at least one of a telephone phone and alight pole. In embodiments, the at least one Internet-of-things deviceis an in-road sensor. In embodiments, the at least oneInternet-of-things device is an in-vehicle thermostat. In embodiments,the at least one Internet-of-things device is a toll booth. Inembodiments, the at least one Internet-of-things device is a streetsign. In embodiments, the at least one Internet-of-things device is atraffic control light. In embodiments, the at least oneInternet-of-things device is a vehicle mounted sensor. In embodiments,the at least one Internet-of-things device is a refueling system. Inembodiments, the at least one Internet-of-things device is a rechargingsystem. In embodiments, the at least one Internet-of-things device is awireless charging station.

An aspect provided herein includes an artificial intelligence system3436 comprising: a first neural network 3422 trained to determine anoperating state 34152 of a vehicle 3410 from data about the vehiclecaptured in an operating environment 34154 of the vehicle, wherein thefirst neural network 3422 operates to identify an operating state 34152of the vehicle by processing information about the vehicle 3410 that iscaptured by at least one Internet-of things device 34150 while thevehicle is operating; a data structure 34156 that facilitatesdetermining operating parameters that influence an operating state of avehicle; a second neural network 3420 that operates to optimize at leastone of the determined operating parameters 34124 of the vehicle based onthe identified operating state 34152 by processing information about astate of a rider 3444 occupying the vehicle 3410, and information thatcorrelates vehicle operation with an effect on rider state.

In embodiments, the improvement in the state of the rider is reflectedin updated data that is descriptive of a state of the rider capturedresponsive to the vehicle operation based on the optimized at least onevehicle operating parameter. In embodiments, the improvement in thestate of the rider is reflected in data captured by at least oneInternet-of-things device 34150 disposed to capture information aboutthe rider 3444 while occupying the vehicle 3410 responsive to theoptimizing. In embodiments, the vehicle 3410 comprises a system forautomating at least one control parameter 34153 of the vehicle. Inembodiments, the vehicle 3410 is at least a semi-autonomous vehicle. Inembodiments, the vehicle 3410 is automatically routed. In embodiments,the vehicle 3410 is a self-driving vehicle. In embodiments, the at leastone Internet-of-things device 34150 is disposed in an operatingenvironment 34154 of the vehicle. In embodiments, the at least oneInternet-of-things device 34150 that captures the data about the vehicleis disposed external to the vehicle. In embodiments, the at least oneInternet-of-things device 34150 is a dashboard camera. In embodiments,the at least one Internet-of-things device 34150 is a mirror camera. Inembodiments, the at least one Internet-of-things device 34150 is amotion sensor. In embodiments, the at least one Internet-of-thingsdevice 34150 is a seat-based sensor system. In embodiments, the at leastone Internet-of-things device 34150 is an IoT enabled lighting system.

In embodiments, the lighting system is a vehicle interior lightingsystem. In embodiments, the lighting system is a headlight lightingsystem. In embodiments, the at least one Internet-of-things device 34150is a traffic light camera or sensor. In embodiments, the at least oneInternet-of-things device 34150 is a roadway camera. In embodiments, theroadway camera is disposed on at least one of a telephone phone and alight pole. In embodiments, the at least one Internet-of-things device34150 is an in-road sensor. In embodiments, the at least oneInternet-of-things device 34150 is an in-vehicle thermostat. Inembodiments, the at least one Internet-of-things device 34150 is a tollbooth. In embodiments, the at least one Internet-of-things device 34150is a street sign. In embodiments, the at least one Internet-of-thingsdevice 34150 is a traffic control light. In embodiments, the at leastone Internet-of-things device 34150 is a vehicle mounted sensor. Inembodiments, the at least one Internet-of-things device 34150 is arefueling system. In embodiments, the at least one Internet-of-thingsdevice 34150 is a recharging system. In embodiments, the at least oneInternet-of-things device 34150 is a wireless charging station.

Referring to FIG. 36 , in embodiments provided herein are transportationsystems 3611 having an artificial intelligence system 3636 forprocessing a sensory input from a wearable device 36157 in a vehicle3610 to determine an emotional state 36126 and optimizing at least oneoperating parameter 36124 of the vehicle 3610 to improve the rider'semotional state 3637. A wearable device 36150, such as any describedthroughout this disclosure, may be used to detect any of the emotionalstates described herein (favorable or unfavorable) and used both as aninput to a real-time control system (such as a model-based, rule-based,or artificial intelligence system of any of the types described herein),such as to indicate an objective to improve an unfavorable state ormaintain a favorable state, as well as a feedback mechanism to train anartificial intelligence system 3636 to configure sets of operatingparameters 36124 to promote or maintain favorable states.

An aspect provided herein includes a system for transportation 3611,comprising: an artificial intelligence system 3636 for processing asensory input from a wearable device 36157 in a vehicle 3610 todetermine an emotional state 36126 of a rider 3644 in the vehicle 3610and optimizing an operating parameter 36124 of the vehicle to improvethe emotional state 3637 of the rider 3644. In embodiments, the vehicleis a self-driving vehicle. In embodiments, the artificial intelligencesystem 3636 is to detect the emotional state 36126 of the rider ridingin the self-driving vehicle by recognition of patterns of emotionalstate indicative data from a set of wearable sensors 36157 worn by therider 3644. In embodiments, the patterns are indicative of at least oneof a favorable emotional state of the rider and an unfavorable emotionalstate of the rider. In embodiments, the artificial intelligence system3636 is to optimize, for achieving at least one of maintaining adetected favorable emotional state of the rider and achieving afavorable emotional state of a rider subsequent to a detection of anunfavorable emotional state, the operating parameter 36124 of thevehicle in response to the detected emotional state of the rider. Inembodiments, the artificial intelligence system 3636 comprises an expertsystem that detects an emotional state of the rider by processing rideremotional state indicative data received from the set of wearablesensors 36157 worn by the rider. In embodiments, the expert systemprocesses the rider emotional state indicative data using at least oneof a training set of emotional state indicators of a set of riders andtrainer-generated rider emotional state indicators. In embodiments, theartificial intelligence system comprises a recurrent neural network 3622that detects the emotional state of the rider.

In embodiments, the recurrent neural network comprises a plurality ofconnected nodes that form a directed cycle, the recurrent neural networkfurther facilitating bi-directional flow of data among the connectednodes. In embodiments, the artificial intelligence system 3636 comprisesa radial basis function neural network 3620 that optimizes theoperational parameter 36124. In embodiments, the optimizing anoperational parameter 36124 is based on a correlation between a vehicleoperating state 3645 and a rider emotional state 3637. In embodiments,the correlation is determined using at least one of a training set ofemotional state indicators of a set of riders and humantrainer-generated rider emotional state indicators. In embodiments, theoperational parameter of the vehicle that is optimized is determined andadjusted to induce a favorable rider emotional state.

In embodiments, the artificial intelligence system 3636 further learnsto classify the patterns of the emotional state indicative data andassociate the patterns to emotional states and changes thereto from atraining data set 36131 sourced from at least one of a stream of datafrom unstructured data sources, social media sources, wearable devices,in-vehicle sensors, a rider helmet, a rider headgear, and a rider voicesystem. In embodiments, the artificial intelligence system 3636 detectsa pattern of the rider emotional state indicative data that indicatesthe emotional state of the rider is changing from a first emotionalstate to a second emotional state, the optimizing of the operationalparameter of the vehicle being response to the indicated change inemotional state. In embodiments, the patterns of rider emotional stateindicative data indicates at least one of an emotional state of therider is changing, an emotional state of the rider is stable, a rate ofchange of an emotional state of the rider, a direction of change of anemotional state of the rider, and a polarity of a change of an emotionalstate of the rider; an emotional state of a rider is changing to anunfavorable state; and an emotional state of a rider is changing to afavorable state.

In embodiments, the operational parameter 36124 that is optimizedaffects at least one of a route of the vehicle, in-vehicle audiocontent, speed of the vehicle, acceleration of the vehicle, decelerationof the vehicle, proximity to objects along the route, and proximity toother vehicles along the route. In embodiments, the artificialintelligence system 3636 interacts with a vehicle control system tooptimize the operational parameter. In embodiments, the artificialintelligence system 3636 further comprises a neural net 3622 thatincludes one or more perceptrons that mimic human senses thatfacilitates determining an emotional state of a rider based on an extentto which at least one of the senses of the rider is stimulated. Inembodiments, the set of wearable sensors 36157 comprises at least two ofa watch, a ring, a wrist band, an arm band, an ankle band, a torso band,a skin patch, a head-worn device, eye glasses, foot wear, a glove, anin-ear device, clothing, headphones, a belt, a finger ring, a thumbring, a toe ring, and a necklace. In embodiments, the artificialintelligence system 3636 uses deep learning for determining patterns ofwearable sensor-generated emotional state indicative data that indicatean emotional state of the rider as at least one of a favorable emotionalstate and an unfavorable emotional state. In embodiments, the artificialintelligence system 3636 is responsive to a rider indicated emotionalstate by at least optimizing the operation parameter to at least one ofachieve and maintain the rider indicated emotional state.

In embodiments, the artificial intelligence system 3636 adapts acharacterization of a favorable emotional state of the rider based oncontext gathered from a plurality of sources including data indicating apurpose of the rider riding in the self-driving vehicle, a time of day,traffic conditions, weather conditions and optimizes the operatingparameter 36124 to at least one of achieve and maintain the adaptedfavorable emotional state. In embodiments, the artificial intelligencesystem 3636 optimizes the operational parameter in real time responsiveto the detecting of an emotional state of the rider. In embodiments, thevehicle is a self-driving vehicle. In embodiments, the artificialintelligence system comprises: a first neural network 3622 to detect theemotional state of the rider through expert system-based processing ofrider emotional state indicative wearable sensor data of a plurality ofwearable physiological condition sensors worn by the rider in thevehicle, the emotional state indicative wearable sensor data indicativeof at least one of a favorable emotional state of the rider and anunfavorable emotional state of the rider; and a second neural network3620 to optimize, for at least one of achieving and maintaining afavorable emotional state of the rider, the operating parameter 36124 ofthe vehicle in response to the detected emotional state of the rider. Inembodiments, the first neural network 3622 is a recurrent neural networkand the second neural network 3620 is a radial basis function neuralnetwork.

In embodiments, the second neural network 3620 optimizes the operationalparameter 36124 based on a correlation between a vehicle operating state3645 and a rider emotional state 3637. In embodiments, the operationalparameter of the vehicle that is optimized is determined and adjusted toinduce a favorable rider emotional state. In embodiments, the firstneural network 3622 further learns to classify patterns of the rideremotional state indicative wearable sensor data and associate thepatterns to emotional states and changes thereto from a training dataset sourced from at least one of a stream of data from unstructured datasources, social media sources, wearable devices, in-vehicle sensors, arider helmet, a rider headgear, and a rider voice system. Inembodiments, the second neural network 3620 optimizes the operationalparameter in real time responsive to the detecting of an emotional stateof the rider by the first neural network 3622. In embodiments, the firstneural network 3622 detects a pattern of the rider emotional stateindicative wearable sensor data that indicates the emotional state ofthe rider is changing from a first emotional state to a second emotionalstate. In embodiments, the second neural network 3620 optimizes theoperational parameter of the vehicle in response to the indicated changein emotional state.

In embodiments, the first neural network 3622 comprises a plurality ofconnected nodes that form a directed cycle, the first neural network3622 further facilitating bi-directional flow of data among theconnected nodes. In embodiments, the first neural network 3622 includesone or more perceptrons that mimic human senses that facilitatesdetermining an emotional state of a rider based on an extent to which atleast one of the senses of the rider is stimulated. In embodiments, therider emotional state indicative wearable sensor data indicates at leastone of an emotional state of the rider is changing, an emotional stateof the rider is stable, a rate of change of an emotional state of therider, a direction of change of an emotional state of the rider, and apolarity of a change of an emotional state of the rider; an emotionalstate of a rider is changing to an unfavorable state; and an emotionalstate of a rider is changing to a favorable state. In embodiments, theoperational parameter that is optimized affects at least one of a routeof the vehicle, in-vehicle audio content, speed of the vehicle,acceleration of the vehicle, deceleration of the vehicle, proximity toobjects along the route, and proximity to other vehicles along theroute. In embodiments, the second neural network 3620 interacts with avehicle control system to adjust the operational parameter. Inembodiments, the first neural network 3622 includes one or moreperceptrons that mimic human senses that facilitates determining anemotional state of a rider based on an extent to which at least one ofthe senses of the rider is stimulated.

In embodiments, the vehicle is a self-driving vehicle. In embodiments,the artificial intelligence system 3636 is to detect a change in theemotional state of the rider riding in the self-driving vehicle at leastin part by recognition of patterns of emotional state indicative datafrom a set of wearable sensors worn by the rider. In embodiments, thepatterns are indicative of at least one of a diminishing of a favorableemotional state of the rider and an onset of an unfavorable emotionalstate of the rider. In embodiments, the artificial intelligence system3636 is to determine at least one operating parameter 36124 of theself-driving vehicle that is indicative of the change in emotional statebased on a correlation of the patterns of emotional state indicativedata with a set of operating parameters of the vehicle. In embodiments,the artificial intelligence system 3636 is to determine an adjustment ofthe at least one operating parameter 36124 for achieving at least one ofrestoring the favorable emotional state of the rider and achieving areduction in the onset of the unfavorable emotional state of a rider.

In embodiments, the correlation of patterns of rider emotionalindicative state wearable sensor data is determined using at least oneof a training set of emotional state wearable sensor indicators of a setof riders and human trainer-generated rider emotional state wearablesensor indicators. In embodiments, the artificial intelligence system3636 further learns to classify the patterns of the emotional stateindicative wearable sensor data and associate the patterns to changes inrider emotional states from a training data set sourced from at leastone of a stream of data from unstructured data sources, social mediasources, wearable devices, in-vehicle sensors, a rider helmet, a riderheadgear, and a rider voice system. In embodiments, the patterns ofrider emotional state indicative wearable sensor data indicates at leastone of an emotional state of the rider is changing, an emotional stateof the rider is stable, a rate of change of an emotional state of therider, a direction of change of an emotional state of the rider, and apolarity of a change of an emotional state of the rider; an emotionalstate of a rider is changing to an unfavorable state; and an emotionalstate of a rider is changing to a favorable state.

In embodiments, the operational parameter determined from a result ofprocessing the rider emotional state indicative wearable sensor dataaffects at least one of a route of the vehicle, in-vehicle audiocontent, speed of the vehicle, acceleration of the vehicle, decelerationof the vehicle, proximity to objects along the route, and proximity toother vehicles along the route. In embodiments, the artificialintelligence system 3636 further interacts with a vehicle control systemfor adjusting the operational parameter. In embodiments, the artificialintelligence system 3636 further comprises a neural net that includesone or more perceptrons that mimic human senses that facilitatedetermining an emotional state of a rider based on an extent to which atleast one of the senses of the rider is stimulated.

In embodiments, the set of wearable sensors comprises at least two of awatch, a ring, a wrist band, an arm band, an ankle band, a torso band, askin patch, a head-worn device, eye glasses, foot wear, a glove, anin-ear device, clothing, headphones, a belt, a finger ring, a thumbring, a toe ring, and a necklace. In embodiments, the artificialintelligence system 3636 uses deep learning for determining patterns ofwearable sensor-generated emotional state indicative data that indicatethe change in the emotional state of the rider. In embodiments, theartificial intelligence system 3636 further determines the change inemotional state of the rider based on context gathered from a pluralityof sources including data indicating a purpose of the rider riding inthe self-driving vehicle, a time of day, traffic conditions, weatherconditions and optimizes the operating parameter 36124 to at least oneof achieve and maintain the adapted favorable emotional state. Inembodiments, the artificial intelligence system 3636 adjusts theoperational parameter in real time responsive to the detecting of achange in rider emotional state.

In embodiments, the vehicle is a self-driving vehicle. In embodiments,the artificial intelligence system 3636 includes: a recurrent neuralnetwork to indicate a change in the emotional state of a rider in theself-driving vehicle by a recognition of patterns of emotional stateindicative wearable sensor data from a set of wearable sensors worn bythe rider. In embodiments, the patterns are indicative of at least oneof a first degree of an favorable emotional state of the rider and asecond degree of an unfavorable emotional state of the rider; and aradial basis function neural network to optimize, for achieving a targetemotional state of the rider, the operating parameter 36124 of thevehicle in response to the indication of the change in the emotionalstate of the rider.

In embodiments, the radial basis function neural network optimizes theoperational parameter based on a correlation between a vehicle operatingstate and a rider emotional state. In embodiments, the target emotionalstate is a favorable rider emotional state and the operational parameterof the vehicle that is optimized is determined and adjusted to inducethe favorable rider emotional state. In embodiments, the recurrentneural network further learns to classify the patterns of emotionalstate indicative wearable sensor data and associate them to emotionalstates and changes thereto from a training data set sourced from atleast one of a stream of data from unstructured data sources, socialmedia sources, wearable devices, in-vehicle sensors, a rider helmet, arider headgear, and a rider voice system. In embodiments, the radialbasis function neural network optimizes the operational parameter inreal time responsive to the detecting of a change in an emotional stateof the rider by the recurrent neural network. In embodiments, therecurrent neural network detects a pattern of the emotional stateindicative wearable sensor data that indicates the emotional state ofthe rider is changing from a first emotional state to a second emotionalstate. In embodiments, the radial basis function neural networkoptimizes the operational parameter of the vehicle in response to theindicated change in emotional state. In embodiments, the recurrentneural network comprises a plurality of connected nodes that form adirected cycle, the recurrent neural network further facilitatingbi-directional flow of data among the connected nodes.

In embodiments, the patterns of emotional state indicative wearablesensor data indicate at least one of an emotional state of the rider ischanging, an emotional state of the rider is stable, a rate of change ofan emotional state of the rider, a direction of change of an emotionalstate of the rider, and a polarity of a change of an emotional state ofthe rider; an emotional state of a rider is changing to an unfavorablestate; and an emotional state of a rider is changing to a favorablestate. In embodiments, the operational parameter that is optimizedaffects at least one of a route of the vehicle, in-vehicle audiocontent, speed of the vehicle, acceleration of the vehicle, decelerationof the vehicle, proximity to objects along the route, and proximity toother vehicles along the route. In embodiments, the radial basisfunction neural network interacts with a vehicle control system toadjust the operational parameter. In embodiments, the recurrent neuralnet includes one or more perceptrons that mimic human senses thatfacilitates determining an emotional state of a rider based on an extentto which at least one of the senses of the rider is stimulated.

In embodiments, the artificial intelligence system 3636 is to maintain afavorable emotional state of the rider through use of a modular neuralnetwork, the modular neural network comprising: a rider emotional statedetermining neural network to process emotional state indicativewearable sensor data of a rider in the vehicle to detect patterns. Inembodiments, the patterns found in the emotional state indicativewearable sensor data are indicative of at least one of a favorableemotional state of the rider and an unfavorable emotional state of therider; an intermediary circuit to convert output data from the rideremotional state determining neural network into vehicle operationalstate data; and a vehicle operational state optimizing neural network toadjust the operating parameter 36124 of the vehicle in response to thevehicle operational state data.

In embodiments, the vehicle operational state optimizing neural networkadjusts an operational parameter of the vehicle for achieving afavorable emotional state of the rider. In embodiments, the vehicleoperational state optimizing neural network optimizes the operationalparameter based on a correlation between a vehicle operating state and arider emotional state. In embodiments, the operational parameter of thevehicle that is optimized is determined and adjusted to induce afavorable rider emotional state. In embodiments, the rider emotionalstate determining neural network further learns to classify the patternsof emotional state indicative wearable sensor data and associate them toemotional states and changes thereto from a training data set sourcedfrom at least one of a stream of data from unstructured data sources,social media sources, wearable devices, in-vehicle sensors, a riderhelmet, a rider headgear, and a rider voice system.

In embodiments, the vehicle operational state optimizing neural networkoptimizes the operational parameter in real time responsive to thedetecting of a change in an emotional state of the rider by the rideremotional state determining neural network. In embodiments, the rideremotional state determining neural network detects a pattern ofemotional state indicative wearable sensor data that indicates theemotional state of the rider is changing from a first emotional state toa second emotional state. In embodiments, the vehicle operational stateoptimizing neural network optimizes the operational parameter of thevehicle in response to the indicated change in emotional state. Inembodiments, the artificial intelligence system 3636 comprises aplurality of connected nodes that forms a directed cycle, the artificialintelligence system 3636 further facilitating bi-directional flow ofdata among the connected nodes. In embodiments, the pattern of emotionalstate indicative wearable sensor data indicate at least one of anemotional state of the rider is changing, an emotional state of therider is stable, a rate of change of an emotional state of the rider, adirection of change of an emotional state of the rider, and a polarityof a change of an emotional state of the rider; an emotional state of arider is changing to an unfavorable state; and an emotional state of arider is changing to a favorable state.

In embodiments, the operational parameter that is optimized affects atleast one of a route of the vehicle, in-vehicle audio content, speed ofthe vehicle, acceleration of the vehicle, deceleration of the vehicle,proximity to objects along the route, and proximity to other vehiclesalong the route. In embodiments, the vehicle operational stateoptimizing neural network interacts with a vehicle control system toadjust the operational parameter. In embodiments, the artificialintelligence system 3636 further comprises a neural net that includesone or more perceptrons that mimic human senses that facilitatesdetermining an emotional state of a rider based on an extent to which atleast one of the senses of the rider is stimulated. In embodiments, therider emotional state determining neural network comprises one or moreperceptrons that mimic human senses that facilitates determining anemotional state of a rider based on an extent to which at least one ofthe senses of the rider is stimulated.

In embodiments, the artificial intelligence system 3636 is to indicate achange in the emotional state of a rider in the vehicle throughrecognition of patterns of emotional state indicative wearable sensordata of the rider in the vehicle; the transportation system furthercomprising: a vehicle control system to control an operation of thevehicle by adjusting a plurality of vehicle operating parameters; and afeedback loop through which the indication of the change in theemotional state of the rider is communicated between the vehicle controlsystem and the artificial intelligence system 3636. In embodiments, thevehicle control system adjusts at least one of the plurality of vehicleoperating parameters responsive to the indication of the change. Inembodiments, the vehicle controls system adjusts the at least one of theplurality of vehicle operational parameters based on a correlationbetween vehicle operational state and rider emotional state.

In embodiments, the vehicle control system adjusts the at least one ofthe plurality of vehicle operational parameters that are indicative of afavorable rider emotional state. In embodiments, the vehicle controlsystem selects an adjustment of the at least one of the plurality ofvehicle operational parameters that is indicative of producing afavorable rider emotional state. In embodiments, the artificialintelligence system 3636 further learns to classify the patterns ofemotional state indicative wearable sensor data and associate them toemotional states and changes thereto from a training data set sourcedfrom at least one of a stream of data from unstructured data sources,social media sources, wearable devices, in-vehicle sensors, a riderhelmet, a rider headgear, and a rider voice system. In embodiments, thevehicle control system adjusts the at least one of the plurality ofvehicle operation parameters in real time.

In embodiments, the artificial intelligence system 3636 further detectsa pattern of the emotional state indicative wearable sensor data thatindicates the emotional state of the rider is changing from a firstemotional state to a second emotional state. In embodiments, the vehicleoperation control system adjusts an operational parameter of the vehiclein response to the indicated change in emotional state. In embodiments,the artificial intelligence system 3636 comprises a plurality ofconnected nodes that form a directed cycle, the artificial intelligencesystem 3636 further facilitating bi-directional flow of data among theconnected nodes. In embodiments, the at least one of the plurality ofvehicle operation parameters that is responsively adjusted affectsoperation of a powertrain of the vehicle and a suspension system of thevehicle.

In embodiments, the radial basis function neural network interacts withthe recurrent neural network via an intermediary component of theartificial intelligence system 3636 that produces vehicle control dataindicative of an emotional state response of the rider to a currentoperational state of the vehicle. In embodiments, the artificialintelligence system 3636 further comprises a modular neural networkcomprising a rider emotional state recurrent neural network forindicating the change in the emotional state of a rider, a vehicleoperational state radial based function neural network, and anintermediary system. In embodiments, the intermediary system processesrider emotional state characterization data from the recurrent neuralnetwork into vehicle control data that the radial based function neuralnetwork uses to interact with the vehicle control system for adjustingthe at least one operational parameter.

In embodiments, the artificial intelligence system 3636 comprises aneural net that includes one or more perceptrons that mimic human sensesthat facilitate determining an emotional state of a rider based on anextent to which at least one of the senses of the rider is stimulated.In embodiments, the recognition of patterns of emotional stateindicative wearable sensor data comprises processing the emotional stateindicative wearable sensor data captured during at least two of beforethe adjusting at least one of the plurality of vehicle operationalparameters, during the adjusting at least one of the plurality ofvehicle operational parameters, and after adjusting at least one of theplurality of vehicle operational parameters.

In embodiments, the artificial intelligence system 3636 indicates achange in the emotional state of the rider responsive to a change in anoperating parameter 36124 of the vehicle by determining a differencebetween a first set of emotional state indicative wearable sensor dataof a rider captured prior to the adjusting at least one of the pluralityof operating parameters and a second set of emotional state indicativewearable sensor data of the rider captured during or after the adjustingat least one of the plurality of operating parameters.

Referring to FIG. 37 , in embodiments provided herein are transportationsystems 3711 having a cognitive system 37158 for managing an advertisingmarket for in-seat advertising for riders 3744 of self-driving vehicles.In embodiments, the cognitive system 37158 takes inputs relating to atleast one parameter 37124 of the vehicle and/or the rider 3744 todetermine at least one of a price, a type and a location of anadvertisement to be delivered within an interface 37133 to a rider 3744in a seat 3728 of the vehicle. As described above in connection withsearch, in-vehicle riders, particularly in self-driving vehicles, may besituationally disposed quite differently toward advertising when ridingin a vehicle than at other times. Bored riders may be more willing towatch advertising content, click on offers or promotions, engage insurveys, or the like. In embodiments, an advertising marketplaceplatform may segment and separately handle advertising placements(including handling bids and asks for advertising placement and thelike) for in-vehicle ads. Such an advertising marketplace platform mayuse information that is unique to a vehicle, such as vehicle type,display type, audio system capabilities, screen size, rider demographicinformation, route information, location information, and the like whencharacterizing advertising placement opportunities, such that bids forin-vehicle advertising placement reflect such vehicle, rider and othertransportation-related parameters. For example, an advertiser may bidfor placement of advertising on in-vehicle display systems ofself-driving vehicles that are worth more than $50,000 and that arerouted north on highway 101 during the morning commute. The advertisingmarketplace platform may be used to configure many such vehicle-relatedplacement opportunities, to handle bidding for such opportunities, toplace advertisements (such as by load-balanced servers that cache theads) and to resolve outcomes. Yield metrics may be tracked and used tooptimize configuration of the marketplace.

An aspect provided herein includes a system for transportation,comprising: a cognitive system 37158 for managing an advertising marketfor in-seat advertising for riders of self-driving vehicles, wherein thecognitive system 37158 takes inputs corresponding to at least oneparameter 37159 of the vehicle or the rider 3744 to determine acharacteristic 37160 of an advertisement to be delivered within aninterface 37133 to a rider 3744 in a seat 3728 of the vehicle, whereinthe characteristic 37160 of the advertisement is selected from the groupconsisting of a price, a category, a location and combinations thereof.

FIG. 38 illustrates a method 3800 of vehicle in-seat advertising inaccordance with embodiments of the systems and methods disclosed herein.At 3802 the method includes taking inputs relating to at least oneparameter of a vehicle. At 3804 the method includes taking inputsrelating to at least one parameter of a rider occupying the vehicle. At3806 the method includes determining at least one of a price,classification, content, and location of an advertisement to bedelivered within an interface of the vehicle to a rider in a seat in thevehicle based on the vehicle-related inputs and the rider-relatedinputs.

Referring to FIG. 37 and FIG. 38 , in embodiments, the vehicle 3710 isautomatically routed. In embodiments, the vehicle 3710 is a self-drivingvehicle. In embodiments, the cognitive system 37158 further determinesat least one of a price, classification, content and location of anadvertisement placement. In embodiments, an advertisement is deliveredfrom an advertiser who places a winning bid. In embodiments, deliveringan advertisement is based on a winning bid. In embodiments, the inputs37162 relating to the at least one parameter of a vehicle includevehicle classification. In embodiments, the inputs 37162 relating to theat least one parameter of a vehicle include display classification. Inembodiments, the inputs 37162 relating to the at least one parameter ofa vehicle include audio system capability. In embodiments, the inputs37162 relating to the at least one parameter of a vehicle include screensize.

In embodiments, the inputs 37162 relating to the at least one parameterof a vehicle include route information. In embodiments, the inputs 37162relating to the at least one parameter of a vehicle include locationinformation. In embodiments, the inputs 37163 relating to the at leastone parameter of a rider include rider demographic information. Inembodiments, the inputs 37163 relating to the at least one parameter ofa rider include rider emotional state. In embodiments, the inputs 37163relating to the at least one parameter of a rider include rider responseto prior in-seat advertising. In embodiments, the inputs 37163 relatingto the at least one parameter of a rider include rider social mediaactivity.

FIG. 39 illustrates a method 3900 of in-vehicle advertising interactiontracking in accordance with embodiments of the systems and methodsdisclosed herein. At 3902 the method includes taking inputs relating toat least one parameter of a vehicle and inputs relating to at least oneparameter of a rider occupying the vehicle. At 3904 the method includesaggregating the inputs across a plurality of vehicles. At 3906 themethod includes using a cognitive system to determine opportunities forin-vehicle advertisement placement based on the aggregated inputs. At3907 the method includes offering the placement opportunities in anadvertising network that facilitates bidding for the placementopportunities. At 3908 the method includes based on a result of thebidding, delivering an advertisement for placement within a userinterface of the vehicle. At 3909 the method includes monitoring vehiclerider interaction with the advertisement presented in the user interfaceof the vehicle.

Referring to FIGS. 37 and 39 , in embodiments, the vehicle 3710comprises a system for automating at least one control parameter of thevehicle. In embodiments, the vehicle 3710 is at least a semi-autonomousvehicle. In embodiments, the vehicle 3710 is automatically routed. Inembodiments, the vehicle 3710 is a self-driving vehicle. In embodiments,an advertisement is delivered from an advertiser who places a winningbid. In embodiments, delivering an advertisement is based on a winningbid. In embodiments, the monitored vehicle rider interaction informationincludes information for resolving click-based payments. In embodiments,the monitored vehicle rider interaction information includes an analyticresult of the monitoring. In embodiments, the analytic result is ameasure of interest in the advertisement. In embodiments, the inputs37162 relating to the at least one parameter of a vehicle includevehicle classification.

In embodiments, the inputs 37162 relating to the at least one parameterof a vehicle include display classification. In embodiments, the inputs37162 relating to the at least one parameter of a vehicle include audiosystem capability. In embodiments, the inputs 37162 relating to the atleast one parameter of a vehicle include screen size. In embodiments,the inputs 37162 relating to the at least one parameter of a vehicleinclude route information. In embodiments, the inputs 37162 relating tothe at least one parameter of a vehicle include location information. Inembodiments, the inputs 37163 relating to the at least one parameter ofa rider include rider demographic information. In embodiments, theinputs 37163 relating to the at least one parameter of a rider includerider emotional state. In embodiments, the inputs 37163 relating to theat least one parameter of a rider include rider response to priorin-seat advertising. In embodiments, the inputs 37163 relating to the atleast one parameter of a rider include rider social media activity.

FIG. 40 illustrates a method 4000 of in-vehicle advertising inaccordance with embodiments of the systems and methods disclosed herein.At 4002 the method includes taking inputs relating to at least oneparameter of a vehicle and inputs relating to at least one parameter ofa rider occupying the vehicle. At 4004 the method includes aggregatingthe inputs across a plurality of vehicles. At 4006 the method includesusing a cognitive system to determine opportunities for in-vehicleadvertisement placement based on the aggregated inputs. At 4008 themethod includes offering the placement opportunities in an advertisingnetwork that facilitates bidding for the placement opportunities. At4009 the method includes based on a result of the bidding, delivering anadvertisement for placement within an interface of the vehicle.

Referring to FIG. 37 and FIG. 40 , in embodiments, the vehicle 3710comprises a system for automating at least one control parameter of thevehicle. In embodiments, the vehicle 3710 is at least a semi-autonomousvehicle. In embodiments, the vehicle 3710 is automatically routed. Inembodiments, the vehicle 3710 is a self-driving vehicle. In embodiments,the cognitive system 37158 further determines at least one of a price,classification, content and location of an advertisement placement. Inembodiments, an advertisement is delivered from an advertiser who placesa winning bid. In embodiments, delivering an advertisement is based on awinning bid. In embodiments, the inputs 37162 relating to the at leastone parameter of a vehicle include vehicle classification.

In embodiments, the inputs 37162 relating to the at least one parameterof a vehicle include display classification. In embodiments, the inputs37162 relating to the at least one parameter of a vehicle include audiosystem capability. In embodiments, the inputs 37162 relating to the atleast one parameter of a vehicle include screen size. In embodiments,the inputs 37162 relating to the at least one parameter of a vehicleinclude route information. In embodiments, the inputs 37162 relating tothe at least one parameter of a vehicle include location information. Inembodiments, the inputs 37163 relating to the at least one parameter ofa rider include rider demographic information. In embodiments, theinputs 37163 relating to the at least one parameter of a rider includerider emotional state. In embodiments, the inputs 37163 relating to theat least one parameter of a rider include rider response to priorin-seat advertising. In embodiments, the inputs 37163 relating to the atleast one parameter of a rider include rider social media activity.

An aspect provided herein includes an advertising system of vehiclein-seat advertising, the advertising system comprising: a cognitivesystem 37158 that takes inputs 37162 relating to at least one parameter37124 of a vehicle 3710 and takes inputs relating to at least oneparameter 37161 of a rider occupying the vehicle, and determines atleast one of a price, classification, content and location of anadvertisement to be delivered within an interface 37133 of the vehicle3710 to a rider 3744 in a seat 3728 in the vehicle 3710 based on thevehicle-related inputs 37162 and the rider-related inputs 37163.

In embodiments, the vehicle 4110 comprises a system for automating atleast one control parameter of the vehicle. In embodiments, the vehicle4110 is at least a semi-autonomous vehicle. In embodiments, the vehicle4110 is automatically routed. In embodiments, the vehicle 4110 is aself-driving vehicle. In embodiments, the inputs 37162 relating to theat least one parameter of a vehicle include vehicle classification. Inembodiments, the inputs 37162 relating to the at least one parameter ofa vehicle include display classification. In embodiments, the inputs37162 relating to the at least one parameter of a vehicle include audiosystem capability. In embodiments, the inputs 37162 relating to the atleast one parameter of a vehicle include screen size. In embodiments,the inputs 37162 relating to the at least one parameter of a vehicleinclude route information. In embodiments, the inputs 37162 relating tothe at least one parameter of a vehicle include location information. Inembodiments, the inputs 37163 relating to the at least one parameter ofa rider include rider demographic information. In embodiments, theinputs 37163 relating to the at least one parameter of a rider includerider emotional state. In embodiments, the inputs 37163 relating to theat least one parameter of a rider include rider response to priorin-seat advertising. In embodiments, the inputs 37163 relating to the atleast one parameter of a rider include rider social media activity.

In embodiments, the advertising system is further to determine a vehicleoperating state from the inputs 37162 related to at least one parameterof the vehicle. In embodiments, the advertisement to be delivered isdetermined based at least in part on the determined vehicle operatingstate. In embodiments, the advertising system is further to determine arider state 37149 from the inputs 37163 related to at least oneparameter of the rider. In embodiments, the advertisement to bedelivered is determined based at least in part on the determined riderstate 37149.

Referring to FIG. 41 , in embodiments provided herein are transportationsystems 4111 having a hybrid cognitive system 41164 for managing anadvertising market for in-seat advertising to riders of vehicles 4110.In embodiments, at least one part of the hybrid cognitive system 41164processes inputs 41162 relating to at least one parameter 41124 of thevehicle to determine a vehicle operating state and at least one otherpart of the cognitive system processes inputs relating to a rider todetermine a rider state. In embodiments, the cognitive system determinesat least one of a price, a type and a location of an advertisement to bedelivered within an interface to a rider in a seat of the vehicle.

An aspect provided herein includes a system for transportation 4111,comprising: a hybrid cognitive system 41164 for managing an advertisingmarket for in-seat advertising to riders 4144 of vehicles 4110. Inembodiments, at least one part 41165 of the hybrid cognitive systemprocesses inputs 41162 corresponding to at least one parameter of thevehicle to determine a vehicle operating state 41168 and at least oneother part 41166 of the cognitive system 41164 processes inputs 41163relating to a rider to determine a rider state 41149. In embodiments,the cognitive system 41164 determines a characteristic 41160 of anadvertisement to be delivered within an interface 41133 to the rider4144 in a seat 4128 of the vehicle 4110. In embodiments, thecharacteristic 41160 of the advertisement is selected from the groupconsisting of a price, a category, a location and combinations thereof.

An aspect provided herein includes an artificial intelligence system4136 for vehicle in-seat advertising, comprising: a first portion 41165of the artificial intelligence system 4136 that determines a vehicleoperating state 41168 of the vehicle by processing inputs 41162 relatingto at least one parameter of the vehicle; a second portion 41166 of theartificial intelligence system 4136 that determines a state 41149 of therider of the vehicle by processing inputs 41163 relating to at least oneparameter of the rider; and a third portion 41167 of the artificialintelligence system 4136 that determines at least one of a price,classification, content and location of an advertisement to be deliveredwithin an interface 41133 of the vehicle to a rider 4144 in a seat inthe vehicle 4110 based on the vehicle (operating) state 41168 and therider state 41149.

In embodiments, the vehicle 4110 comprises a system for automating atleast one control parameter of the vehicle. In embodiments, the vehicleis at least a semi-autonomous vehicle. In embodiments, the vehicle isautomatically routed. In embodiments, the vehicle is a self-drivingvehicle. In embodiments, the cognitive system 41164 further determinesat least one of a price, classification, content and location of anadvertisement placement. In embodiments, an advertisement is deliveredfrom an advertiser who places a winning bid. In embodiments, deliveringan advertisement is based on a winning bid. In embodiments, the inputsrelating to the at least one parameter of a vehicle include vehicleclassification.

In embodiments, the inputs relating to the at least one parameter of avehicle include display classification. In embodiments, the inputsrelating to the at least one parameter of a vehicle include audio systemcapability. In embodiments, the inputs relating to the at least oneparameter of a vehicle include screen size. In embodiments, the inputsrelating to the at least one parameter of a vehicle include routeinformation. In embodiments, the inputs relating to the at least oneparameter of a vehicle include location information. In embodiments, theinputs relating to the at least one parameter of a rider include riderdemographic information. In embodiments, the inputs relating to the atleast one parameter of a rider include rider emotional state. Inembodiments, the inputs relating to the at least one parameter of arider include rider response to prior in-seat advertising. Inembodiments, the inputs relating to the at least one parameter of arider include rider social media activity.

FIG. 42 illustrates a method 4200 of in-vehicle advertising interactiontracking in accordance with embodiments of the systems and methodsdisclosed herein. At 4202 the method includes taking inputs relating toat least one parameter of a vehicle and inputs relating to at least oneparameter of a rider occupying the vehicle. At 4204 the method includesaggregating the inputs across a plurality of vehicles. At 4206 themethod includes using a hybrid cognitive system to determineopportunities for in-vehicle advertisement placement based on theaggregated inputs. At 4207 the method includes offering the placementopportunities in an advertising network that facilitates bidding for theplacement opportunities. At 4208 the method includes based on a resultof the bidding, delivering an advertisement for placement within a userinterface of the vehicle. At 4209 the method includes monitoring vehiclerider interaction with the advertisement presented in the user interfaceof the vehicle.

Referring to FIG. 41 and FIG. 42 , in embodiments, the vehicle 4110comprises a system for automating at least one control parameter of thevehicle. In embodiments, the vehicle 4110 is at least a semi-autonomousvehicle. In embodiments, the vehicle 4110 is automatically routed. Inembodiments, the vehicle 4110 is a self-driving vehicle. In embodiments,a first portion 41165 of the hybrid cognitive system 41164 determines anoperating state of the vehicle by processing inputs relating to at leastone parameter of the vehicle. In embodiments, a second portion 41166 ofthe hybrid cognitive system 41164 determines a state 41149 of the riderof the vehicle by processing inputs relating to at least one parameterof the rider. In embodiments, a third portion 41167 of the hybridcognitive system 41164 determines at least one of a price,classification, content and location of an advertisement to be deliveredwithin an interface of the vehicle to a rider in a seat in the vehiclebased on the vehicle state and the rider state. In embodiments, anadvertisement is delivered from an advertiser who places a winning bid.In embodiments, delivering an advertisement is based on a winning bid.In embodiments, the monitored vehicle rider interaction informationincludes information for resolving click-based payments. In embodiments,the monitored vehicle rider interaction information includes an analyticresult of the monitoring. In embodiments, the analytic result is ameasure of interest in the advertisement. In embodiments, the inputs41162 relating to the at least one parameter of a vehicle includevehicle classification. In embodiments, the inputs 41162 relating to theat least one parameter of a vehicle include display classification. Inembodiments, the inputs 41162 relating to the at least one parameter ofa vehicle include audio system capability. In embodiments, the inputs41162 relating to the at least one parameter of a vehicle include screensize. In embodiments, the inputs 41162 relating to the at least oneparameter of a vehicle include route information. In embodiments, theinputs 41162 relating to the at least one parameter of a vehicle includelocation information. In embodiments, the inputs 41163 relating to theat least one parameter of a rider include rider demographic information.In embodiments, the inputs 41163 relating to the at least one parameterof a rider include rider emotional state. In embodiments, the inputs41163 relating to the at least one parameter of a rider include riderresponse to prior in-seat advertising. In embodiments, the inputs 41163relating to the at least one parameter of a rider include rider socialmedia activity.

FIG. 43 illustrates a method 4300 of in-vehicle advertising inaccordance with embodiments of the systems and methods disclosed herein.At 4302 the method includes taking inputs relating to at least oneparameter of a vehicle and inputs relating to at least one parameter ofa rider occupying the vehicle. At 4304 the method includes aggregatingthe inputs across a plurality of vehicles. At 4306 the method includesusing a hybrid cognitive system to determine opportunities forin-vehicle advertisement placement based on the aggregated inputs. At4308 the method includes offering the placement opportunities in anadvertising network that facilitates bidding for the placementopportunities. At 4309 the method includes based on a result of thebidding, delivering an advertisement for placement within an interfaceof the vehicle.

Referring to FIG. 41 and FIG. 43 , in embodiments, the vehicle 4110comprises a system for automating at least one control parameter of thevehicle. In embodiments, the vehicle 4110 is at least a semi-autonomousvehicle. In embodiments, the vehicle 4110 is automatically routed. Inembodiments, the vehicle 4110 is a self-driving vehicle. In embodiments,a first portion 41165 of the hybrid cognitive system 41164 determines anoperating state 41168 of the vehicle by processing inputs 41162 relatingto at least one parameter of the vehicle. In embodiments, a secondportion 41166 of the hybrid cognitive system 41164 determines a state41149 of the rider of the vehicle by processing inputs 41163 relating toat least one parameter of the rider. In embodiments, a third portion41167 of the hybrid cognitive system 41164 determines at least one of aprice, classification, content and location of an advertisement to bedelivered within an interface 41133 of the vehicle 4110 to a rider 4144in a seat 4128 in the vehicle 4110 based on the vehicle (operating)state 41168 and the rider state 41149. In embodiments, an advertisementis delivered from an advertiser who places a winning bid. Inembodiments, delivering an advertisement is based on a winning bid. Inembodiments, the inputs 41162 relating to the at least one parameter ofa vehicle include vehicle classification. In embodiments, the inputs41162 relating to the at least one parameter of a vehicle includedisplay classification. In embodiments, the inputs 41162 relating to theat least one parameter of a vehicle include audio system capability. Inembodiments, the inputs 41162 relating to the at least one parameter ofa vehicle include screen size. In embodiments, the inputs 41162 relatingto the at least one parameter of a vehicle include route information. Inembodiments, the inputs 41162 relating to the at least one parameter ofa vehicle include location information. In embodiments, the inputs 41163relating to the at least one parameter of a rider include riderdemographic information. In embodiments, the inputs 41163 relating tothe at least one parameter of a rider include rider emotional state. Inembodiments, the inputs 41163 relating to the at least one parameter ofa rider include rider response to prior in-seat advertising. Inembodiments, the inputs 41163 relating to the at least one parameter ofa rider include rider social media activity.

Referring to FIG. 44 , in embodiments provided herein are transportationsystems 4411 having a motorcycle helmet 44170 that is configured toprovide an augmented reality experience based on registration of thelocation and orientation of the wearer 44172 in an environment 44171.

An aspect provided herein includes a system for transportation 4411,comprising: a motorcycle helmet 44170 to provide an augmented realityexperience based on registration of a location and orientation of awearer 44172 of the helmet 44170 in an environment 44171.

An aspect provided herein includes a motorcycle helmet 44170 comprising:a data processor 4488 configured to facilitate communication between arider 44172 wearing the helmet 44170 and a motorcycle 44169, themotorcycle 44169 and the helmet 44170 communicating location andorientation 44173 of the motorcycle 44169; and an augmented realitysystem 44174 with a display 44175 disposed to facilitate presenting anaugmentation of content in an environment 44171 of a rider wearing thehelmet, the augmentation responsive to a registration of thecommunicated location and orientation 44128 of the motorcycle 44169. Inembodiments, at least one parameter of the augmentation is determined bymachine learning on at least one input relating to at least one of therider 44172 and the motorcycle 44180.

In embodiments, the motorcycle 44169 comprises a system for automatingat least one control parameter of the motorcycle. In embodiments, themotorcycle 44169 is at least a semi-autonomous motorcycle. Inembodiments, the motorcycle 44169 is automatically routed. Inembodiments, the motorcycle 44169 is a self-driving motorcycle. Inembodiments, the content in the environment is content that is visiblein a portion of a field of view of the rider wearing the helmet. Inembodiments, the machine learning on the input of the rider determinesan emotional state of the rider and a value for the at least oneparameter is adapted responsive to the rider emotional state. Inembodiments, the machine learning on the input of the motorcycledetermines an operational state of the motorcycle and a value for the atleast one parameter is adapted responsive to the motorcycle operationalstate. In embodiments, the helmet 44170 further comprises a motorcycleconfiguration expert system 44139 for recommending an adjustment of avalue of the at least one parameter 44156 to the augmented realitysystem responsive to the at least one input.

An aspect provided herein includes a motorcycle helmet augmented realitysystem comprising: a display 44175 disposed to facilitate presenting anaugmentation of content in an environment of a rider wearing the helmet;a circuit 4488 for registering at least one of location and orientationof a motorcycle that the rider is riding; a machine learning circuit44179 that determines at least one augmentation parameter 44156 byprocessing at least one input relating to at least one of the rider44163 and the motorcycle 44180; and a reality augmentation circuit 4488that, responsive to the registered at least one of a location andorientation of the motorcycle generates an augmentation element 44177for presenting in the display 44175, the generating based at least inpart on the determined at least one augmentation parameter 44156.

In embodiments, the motorcycle 44169 comprises a system for automatingat least one control parameter of the motorcycle. In embodiments, themotorcycle 44169 is at least a semi-autonomous motorcycle. Inembodiments, the motorcycle 44169 is automatically routed. Inembodiments, the motorcycle 44169 is a self-driving motorcycle. Inembodiments, the content 44176 in the environment is content that isvisible in a portion of a field of view of the rider 44172 wearing thehelmet. In embodiments, the machine learning on the input of the riderdetermines an emotional state of the rider and a value for the at leastone parameter is adapted responsive to the rider emotional state. Inembodiments, the machine learning on the input of the motorcycledetermines an operational state of the motorcycle and a value for the atleast one parameter is adapted responsive to the motorcycle operationalstate.

In embodiments, the helmet further comprises a motorcycle configurationexpert system 44139 for recommending an adjustment of a value of the atleast one parameter 44156 to the augmented reality system 4488responsive to the at least one input.

In embodiments, leveraging network technologies for a transportationsystem may support a cognitive collective charging or refueling plan forvehicles in the transportation system. Such a transportation system mayinclude an artificial intelligence system for taking inputs relating toa plurality of vehicles, such as self-driving vehicles, and determiningat least one parameter of a re-charging or refueling plan for at leastone of the plurality of vehicles based on the inputs.

In embodiments, the transportation system may be a vehicletransportation system. Such a vehicle transportation system may includea network-enabled vehicle information ingestion port 4532 that mayprovide a network (e.g., Internet and the like) interface through whichinputs, such as inputs comprising operational state and energyconsumption information from at least one of a plurality ofnetwork-enabled vehicles 4510 may be gathered. In embodiments, suchinputs may be gathered in real time as the plurality of network-enabledvehicles 4510 connect to and deliver vehicle operational state, energyconsumption and other related information. In embodiments, the inputsmay relate to vehicle energy consumption and may be determined from abattery charge state of a portion of the plurality of vehicles. Theinputs may include a route plan for the vehicle, an indicator of thevalue of charging of the vehicle, and the like. The inputs may includepredicted traffic conditions for the plurality of vehicles. Thetransportation system may also include vehicle charging or refuelinginfrastructure that may include one or more vehicle charginginfrastructure control system(s) 4534. These control system(s) 4534 mayreceive the operational state and energy consumption information for theplurality of network-enabled vehicles 4510 via the ingestion port 4532or directly through a common or set of connected networks, such as theInternet and the like. Such a transportation system may further includean artificial intelligence system 4536 that may be functionallyconnected with the vehicle charging infrastructure control system(s)4534 that, for example, responsive to the receiving of the operationalstate and energy consumption information, may determine, provide, adjustor create at least one charging plan parameter 4514 upon which acharging plan 4512 for at least a portion of the plurality ofnetwork-enabled vehicles 4510 is dependent. This dependency may yieldchanges in the application of the charging plan 4512 by the controlsystem(s) 4534, such as when a processor of the control system(s) 4534executes a program derived from or based on the charging plan 4512. Thecharging infrastructure control system(s) 4534 may include a cloud-basedcomputing system remote from charging infrastructure systems (e.g.,remote from an electric vehicle charging kiosk and the like); it mayalso include a local charging infrastructure system 4538 that may bedisposed with and/or integrated with an infrastructure element, such asa fuel station, a charging kiosk and the like. In embodiments, theartificial intelligence system 4536 may interface and coordinate withthe cloud-based system 4534, the local charging infrastructure system4538 or both. In embodiments, coordination of the cloud-based system maytake on a different form of interfacing, such as providing parametersthat affect more than one charging kiosk and the like than maycoordination with the local charging infrastructure system 4538, whichmay provide information that the local system could use to adaptcharging system control commands and the like that may be provided from,for example, a cloud-based control system 4534. In an example, acloud-based control system (that may control only a portion, such as alocalized set, of available charging/refueling infrastructure devices)may respond to the charging plan parameter 4514 of the artificialintelligence system 4536 by setting a charging rate that facilitateshighly parallel vehicle charging. However, the local charginginfrastructure system 4538 may adapt this control plan, such as based ona control plan parameter provided to it by the artificial intelligencesystem 4536, to permit a different charging rate (e.g., a fastercharging rate), such as for a brief period to accommodate anaccumulation of vehicles queued up or estimated to use a local chargingkiosk in the period. In this way, an adjustment to the at least oneparameter 4514 that when made to the charge infrastructure operationplan 4512 ensures that the at least one of the plurality of vehicles4510 has access to energy renewal in a target energy renewal geographicregion 4516.

In embodiments, a charging or refueling plan may have a plurality ofparameters that may impact a wide range of transportation aspectsranging from vehicle-specific to vehicle group-specific to vehiclelocation-specific and infrastructure impacting aspects. Therefore, aparameter of the plan may impact or relate to any of vehicle routing tocharging infrastructure, amount of charge permitted to be provided,duration of time or rate for charging, battery conditions or state,battery charging profile, time required to charge to a minimum valuethat may be based on consumption needs of the vehicle(s), market valueof charging, indicators of market value, market price, infrastructureprovider profit, bids or offers for providing fuel or electricity to oneor more charging or refueling infrastructure kiosks, available supplycapacity, recharge demand (local, regional, system wide), and the like.

In embodiments, to facilitate a cognitive charging or refueling plan,the transportation system may include a recharging plan update facilitythat interacts with the artificial intelligence system 4536 to apply anadjustment value 4524 to the at least one of the plurality of rechargingplan parameters 4514. An adjustment value 4524 may be further adjustedbased on feedback of applying the adjustment value. In embodiments, thefeedback may be used by the artificial intelligence system 4534 tofurther adjust the adjustment value. In an example, feedback may impactthe adjustment value applied to charging or refueling infrastructurefacilities in a localized way, such as for a target recharginggeographic region 4516 or geographic range relative to one or morevehicles. In embodiments, providing a parameter adjustment value mayfacilitate optimizing consumption of a remaining battery charge state ofat least one of the plurality of vehicles.

By processing energy-related consumption, demand, availability, andaccess information and the like, the artificial intelligence system 4536may optimize aspects of the transportation system, such as vehicleelectricity usage as shown in the box at 4526. The artificialintelligence system 4536 may further optimize at least one of rechargingtime, location, and amount. In an example, a recharging plan parameterthat may be configured and updated based on feedback may be a routingparameter for the at least one of the plurality of vehicles as shown inthe box at 4526.

The artificial intelligence system 4536 may further optimize atransportation system charging or refueling control plan parameter 4514to, for example, accommodate near-term charging needs for the pluralityof rechargeable vehicles 4510 based on the optimized at least oneparameter. The artificial intelligence system 4536 may execute anoptimizing algorithm that may calculate energy parameters (includingvehicle and non-vehicle energy), optimizes electricity usage for atleast vehicles and/or charging or refueling infrastructure, andoptimizes at least one charging or refueling infrastructure-specificrecharging time, location, and amount.

In embodiments, the artificial intelligence system 4534 may predict ageolocation 4518 of one or more vehicles within a geographic region4516. The geographic region 4516 may include vehicles that are currentlylocated in or predicted to be in the region and optionally may requireor prefer recharging or refueling. As an example of predictinggeolocation and its impact on a charging plan, a charging plan parametermay include allocation of vehicles currently in or predicted to be inthe region to charging or refueling infrastructure in the geographicregion 4516. In embodiments, geolocation prediction may includereceiving inputs relating to charging states of a plurality of vehicleswithin or predicted to be within a geolocation range so that theartificial intelligence system can optimize at least one charging planparameter 4514 based on a prediction of geolocations of the plurality ofvehicles.

There are many aspects of a charging plan that may be impacted. Someaspects may be financial related, such as automated negotiation of atleast one of a duration, a quantity and a price for charging orrefueling a vehicle.

The transportation system cognitive charging plan system may include theartificial intelligence system being configured with a hybrid neuralnetwork. A first neural network 4522 of the hybrid neural network may beused to process inputs relating to charge or fuel states of theplurality of vehicles (directly received from the vehicles or throughthe vehicle information port 4532) and a second neural network 4520 ofthe hybrid neural network is used to process inputs relating to chargingor refueling infrastructure and the like. In embodiments, the firstneural network 4522 may process inputs comprising vehicle route andstored energy state information for a plurality of vehicles to predictfor at least one of the plurality of vehicles a target energy renewalregion. The second neural network 4520 may process vehicle energyrenewal infrastructure usage and demand information for vehicle energyrenewal infrastructure facilities within the target energy renewalregion to determine at least one parameter 4514 of a chargeinfrastructure operational plan 4512 that facilitates access by the atleast one of the plurality vehicles to renewal energy in the targetenergy renewal region 4516. In embodiments, the first and/or secondneural networks may be configured as any of the neural networksdescribed herein including without limitation convolutional typenetworks.

In embodiments, a transportation system may be distributed and mayinclude an artificial intelligence system 4536 for taking inputsrelating to a plurality of vehicles 4510 and determining at least oneparameter 4514 of a re-charging and refueling plan 4512 for at least oneof the plurality of vehicles based on the inputs. In embodiments, suchinputs may be gathered in real time as plurality of vehicles 4510connect to and deliver vehicle operational state, energy consumption andother related information. In embodiments, the inputs may relate tovehicle energy consumption and may be determined from a battery chargestate of a portion of the plurality of vehicles. The inputs may includea route plan for the vehicle, an indicator of the value of charging ofthe vehicle, and the like. The inputs may include predicted trafficconditions for the plurality of vehicles. The distributed transportationsystem may also include cloud-based and vehicle-based systems thatexchange information about the vehicle, such as energy consumption andoperational information and information about the transportation system,such as recharging or refueling infrastructure. The artificialintelligence system may respond to transportation system and vehicleinformation shared by the cloud and vehicle-based system with controlparameters that facilitate executing a cognitive charging plan for atleast a portion of charging or refueling infrastructure of thetransportation system. The artificial intelligence system 4536 maydetermine, provide, adjust or create at least one charging planparameter 4514 upon which a charging plan 4512 for at least a portion ofthe plurality of vehicles 4510 is dependent. This dependency may yieldchanges in the execution of the charging plan 4512 by at least one thecloud-based and vehicle-based systems, such as when a processor executesa program derived from or based on the charging plan 4512.

In embodiments, an artificial intelligence system of a transportationsystem may facilitate execution of a cognitive charging plan by applyinga vehicle recharging facility utilization optimization algorithm to aplurality of rechargeable vehicle-specific inputs, e.g., currentoperating state data for rechargeable vehicles present in a targetrecharging range of one of the plurality of rechargeable vehicles. Theartificial intelligence system may also evaluate an impact of aplurality of recharging plan parameters on recharging infrastructure ofthe transportation system in the target recharging range. The artificialintelligence system may select at least one of the plurality ofrecharging plan parameters that facilitates, for example optimizingenergy usage by the plurality of rechargeable vehicles and generate anadjustment value for the at least one of the plurality of rechargingplan parameters. The artificial intelligence system may further predicta near-term need for recharging for a portion of the plurality ofrechargeable vehicles within the target region based on, for example,operational status of the plurality of rechargeable vehicles that may bedetermined from the rechargeable vehicle-specific inputs. Based on thisprediction and near-term recharging infrastructure availability andcapacity information, the artificial intelligence system may optimize atleast one parameter of the recharging plan. In embodiments, theartificial intelligence system may operate a hybrid neural network forthe predicting and parameter selection or adjustment. In an example, afirst portion of the hybrid neural network may process inputs thatrelates to route plans for one more rechargeable vehicles. In theexample, a second portion of the hybrid neural network that is distinctfrom the first portion may process inputs relating to recharginginfrastructure within a recharging range of at least one of therechargeable vehicles. In this example, the second distinct portion ofthe hybrid neural net predicts the geolocation of a plurality ofvehicles within the target region. To facilitate execution of therecharging plan, the parameter may impact an allocation of vehicles toat least a portion of recharging infrastructure within the predictedgeographic region.

In embodiments, vehicles described herein may comprise a system forautomating at least one control parameter of the vehicle. The vehiclesmay further at least operate as a semi-autonomous vehicle. The vehiclesmay be automatically routed. Also, the vehicles, recharging andotherwise may be self-driving vehicles.

In embodiments, leveraging network technologies for a transportationsystem may support a cognitive collective charging or refueling plan forvehicles in the transportation system. Such a transportation system mayinclude an artificial intelligence system for taking inputs relating tobattery status of a plurality of vehicles, such as self-driving vehiclesand determining at least one parameter of a re-charging and/or refuelingplan for optimizing battery operation of at least one of the pluralityof vehicles based on the inputs.

In embodiments, such a vehicle transportation system may include anetwork-enabled vehicle information ingestion port 4632 that may providea network (e.g., Internet and the like) interface through which inputs,such as inputs comprising operational state and energy consumptioninformation and battery state from at least one of a plurality ofnetwork-enabled vehicles 4610 may be gathered. In embodiments, suchinputs may be gathered in real time as a plurality of vehicles 4610connect to a network and deliver vehicle operational state, energyconsumption, battery state and other related information. Inembodiments, the inputs may relate to vehicle energy consumption and mayinclude a battery charge state of a portion of the plurality ofvehicles. The inputs may include a route plan for the vehicle, anindicator of the value of charging of the vehicle, and the like. Theinputs may include predicted traffic conditions for the plurality ofvehicles. The transportation system may also include vehicle charging orrefueling infrastructure that may include one or more vehicle charginginfrastructure control systems 4634. These control systems may receivethe battery status information and the like for the plurality ofnetwork-enabled vehicles 4610 via the ingestion port 4632 and/ordirectly through a common or set of connected networks, such as anInternet infrastructure including wireless networks and the like. Such atransportation system may further include an artificial intelligencesystem 4636 that may be functionally connected with the vehicle charginginfrastructure control systems that may, based on at least the batterystatus information from the portion of the plurality of vehiclesdetermine, provide, adjust or create at least one charging planparameter 4614 upon which a charging plan 4612 for at least a portion ofthe plurality of network-enabled vehicles 4610 is dependent. Thisparameter dependency may yield changes in the application of thecharging plan 4612 by the control system(s) 4634, such as when aprocessor of the control system(s) 4634 executes a program derived fromor based on the charging plan 4612. These changes may be applied tooptimize anticipated battery usage of one or more of the vehicles. Theoptimizing may be vehicle-specific, aggregated across a set of vehicles,and the like. The charging infrastructure control system(s) 4634 mayinclude a cloud-based computing system remote from charginginfrastructure systems (e.g., remote from an electric vehicle chargingkiosk and the like); it may also include a local charging infrastructuresystem 4638 that may be disposed with and/or integrated into aninfrastructure element, such as a fuel station, a charging kiosk and thelike. In embodiments, the artificial intelligence system 4636 mayinterface with the cloud-based system 4634, the local charginginfrastructure system 4638 or both. In embodiments, the artificialintelligence system may interface with individual vehicles to facilitateoptimizing anticipated battery usage. In embodiments, interfacing withthe cloud-based system may affect infrastructure-wide impact of acharging plan, such as providing parameters that affect more than onecharging kiosk. Interfacing with the local charging infrastructuresystem 4638 may include providing information that the local systemcould use to adapt charging system control commands and the like thatmay be provided from, for example, a regional or broader control system,such as a cloud-based control system 4634. In an example, a cloud-basedcontrol system (that may control only a target or geographic region,such as a localized set, a town, a county, a city, a ward, county andthe like of available charging or refueling infrastructure devices) mayrespond to the charging plan parameter 4614 of the artificialintelligence system 4636 by setting a charging rate that facilitateshighly parallel vehicle charging so that vehicle battery usage can beoptimized. However, the local charging infrastructure system 4638 mayadapt this control plan, such as based on a control plan parameterprovided to it by the artificial intelligence system 4636, to permit adifferent charging rate (e.g., a faster charging rate), such as for abrief period to accommodate an accumulation of vehicles for whichanticipated battery usage is not yet optimized. In this way, anadjustment to the at least one parameter 4614 that when made to thecharge infrastructure operation plan 4612 ensures that the at least oneof the plurality of vehicles 4610 has access to energy renewal in atarget energy renewal region 4616. In embodiments, a target energyrenewal region may be defined by a geofence that may be configured by anadministrator of the region. In an example an administrator may havecontrol or responsibility for a jurisdiction (e.g., a township, and thelike). In the example, the administrator may configure a geofence for aregion that is substantially congruent with the jurisdiction.

In embodiments, a charging or refueling plan may have a plurality ofparameters that may impact a wide range of transportation aspectsranging from vehicle-specific to vehicle group-specific to vehiclelocation-specific and infrastructure impacting aspects. Therefore, aparameter of the plan may impact or relate to any of vehicle routing tocharging infrastructure, amount of charge permitted to be provided,duration of time or rate for charging, battery conditions or state,battery charging profile, time required to charge to a minimum valuethat may be based on consumption needs of the vehicle(s), market valueof charging, indicators of market value, market price, infrastructureprovider profit, bids or offers for providing fuel or electricity to oneor more charging or refueling infrastructure kiosks, available supplycapacity, recharge demand (local, regional, system wide), maximum energyusage rate, time between battery charging, and the like.

In embodiments, to facilitate a cognitive charging or refueling plan,the transportation system may include a recharging plan update facilitythat interacts with the artificial intelligence system 4636 to apply anadjustment value 4624 to the at least one of the plurality of rechargingplan parameters 4614. An adjustment value 4624 may be further adjustedbased on feedback of applying the adjustment value. In embodiments, thefeedback may be used by the artificial intelligence system 4634 tofurther adjust the adjustment value. In an example, feedback may impactthe adjustment value applied to charging or refueling infrastructurefacilities in a localized way, such as impacting only a set of vehiclesthat are impacted by or projected to be impacted by a traffic jam sothat their battery operation is optimized, so as to, for example, ensurethat they have sufficient battery power throughout the duration of thetraffic jam. In embodiments, providing a parameter adjustment value mayfacilitate optimizing consumption of a remaining battery charge state ofat least one of the plurality of vehicles.

By processing energy-related consumption, demand, availability, andaccess information and the like, the artificial intelligence system 4636may optimize aspects of the transportation system, such as vehicleelectricity usage as shown in the box at 4626. The artificialintelligence system 4636 may further optimize at least one of rechargingtime, location, and amount as shown in the box at 4626. In an example arecharging plan parameter that may be configured and updated based onfeedback may be a routing parameter for the at least one of theplurality of vehicles.

The artificial intelligence system 4636 may further optimize atransportation system charging or refueling control plan parameter 4614to, for example accommodate near-term charging needs for the pluralityof rechargeable vehicles 4610 based on the optimized at least oneparameter. The artificial intelligence system 4636 may execute a vehiclerecharging optimizing algorithm that may calculate energy parameters(including vehicle and non-vehicle energy) that may impact ananticipated battery usage, optimizes electricity usage for at leastvehicles and/or charging or refueling infrastructure, and optimizes atleast one charging or refueling infrastructure-specific recharging time,location, and amount.

In embodiments, the artificial intelligence system 4634 may predict ageolocation 4618 of one or more vehicles within a geographic region4616. The geographic region 4616 may include vehicles that are currentlylocated in or predicted to be in the region and optionally may requireor prefer recharging or refueling. As an example of predictinggeolocation and its impact on a charging plan, a charging plan parametermay include allocation of vehicles currently in or predicted to be inthe region to charging or refueling infrastructure in the geographicregion 4616. In embodiments, geolocation prediction may includereceiving inputs relating to battery and battery charging states andrecharging needs of a plurality of vehicles within or predicted to bewithin a geolocation range so that the artificial intelligence systemcan optimize at least one charging plan parameter 4614 based on aprediction of geolocations of the plurality of vehicles.

There are many aspects of a charging plan that may be impacted. Someaspects may be financial related, such as automated negotiation of atleast one of a duration, a quantity and a price for charging orrefueling a vehicle.

The transportation system cognitive charging plan system may include theartificial intelligence system being configured with a hybrid neuralnetwork. A first neural network 4622 of the hybrid neural network may beused to process inputs relating to battery charge or fuel states of theplurality of vehicles (directly received from the vehicles or throughthe vehicle information port 4632) and a second neural network 4620 ofthe hybrid neural network is used to process inputs relating to chargingor refueling infrastructure and the like. In embodiments, the firstneural network 4622 may process inputs comprising information about acharging system of the vehicle and vehicle route and stored energy stateinformation for a plurality of vehicles to predict for at least one ofthe plurality of vehicles a target energy renewal region. The secondneural network 4620 may further predict a geolocation of a portion ofthe plurality of vehicles relative to another vehicle or set ofvehicles. The second neural network 4620 may process vehicle energyrenewal infrastructure usage and demand information for vehicle energyrenewal infrastructure facilities within the target energy renewalregion to determine at least one parameter 4614 of a chargeinfrastructure operational plan 4612 that facilitates access by the atleast one of the plurality vehicles to renewal energy in the targetenergy renewal region 4616. In embodiments, the first and/or secondneural networks may be configured as any of the neural networksdescribed herein including without limitation convolutional typenetworks.

In embodiments, a transportation system may be distributed and mayinclude an artificial intelligence system 4636 for taking inputsrelating to a plurality of vehicles 4610 and determining at least oneparameter 4614 of a re-charging and refueling plan 4612 for at least oneof the plurality of vehicles based on the inputs. In embodiments, suchinputs may be gathered in real time as plurality of vehicles 4610connect to a network and deliver vehicle operational state, energyconsumption and other related information. In embodiments, the inputsmay relate to vehicle energy consumption and may be determined from abattery charge state of a portion of the plurality of vehicles. Theinputs may include a route plan for the vehicle, an indicator of thevalue of charging of the vehicle, and the like. The inputs may includepredicted traffic conditions for the plurality of vehicles. Thedistributed transportation system may also include cloud-based andvehicle-based systems that exchange information about the vehicle, suchas energy consumption and operational information and information aboutthe transportation system, such as recharging or refuelinginfrastructure. The artificial intelligence system may respond totransportation system and vehicle information shared by the cloud andvehicle-based system with control parameters that facilitate executing acognitive charging plan for at least a portion of charging or refuelinginfrastructure of the transportation system. The artificial intelligencesystem 4636 may determine, provide, adjust or create at least onecharging plan parameter 4614 upon which a charging plan 4612 for atleast a portion of the plurality of vehicles 4610 is dependent. Thisdependency may yield changes in the execution of the charging plan 4612by at least one the cloud-based and vehicle-based systems, such as whena processor executes a program derived from or based on the chargingplan 4612.

In embodiments, an artificial intelligence system of a transportationsystem may facilitate execution of a cognitive charging plan by applyinga vehicle recharging facility utilization of a vehicle battery operationoptimization algorithm to a plurality of rechargeable vehicle-specificinputs, e.g., current operating state data for rechargeable vehiclespresent in a target recharging range of one of the plurality ofrechargeable vehicles. The artificial intelligence system may alsoevaluate an impact of a plurality of recharging plan parameters onrecharging infrastructure of the transportation system in the targetrecharging range. The artificial intelligence system may select at leastone of the plurality of recharging plan parameters that facilitates, forexample optimizing energy usage by the plurality of rechargeablevehicles and generate an adjustment value for the at least one of theplurality of recharging plan parameters. The artificial intelligencesystem may further predict a near-term need for recharging for a portionof the plurality of rechargeable vehicles within the target region basedon, for example, operational status of the plurality of rechargeablevehicles that may be determined from the rechargeable vehicle-specificinputs. Based on this prediction and near-term recharging infrastructureavailability and capacity information, the artificial intelligencesystem may optimize at least one parameter of the recharging plan. Inembodiments, the artificial intelligence system may operate a hybridneural network for the predicting and parameter selection or adjustment.In an example, a first portion of the hybrid neural network may processinputs that relates to route plans for one more rechargeable vehicles.In the example, a second portion of the hybrid neural network that isdistinct from the first portion may process inputs relating torecharging infrastructure within a recharging range of at least one ofthe rechargeable vehicles. In this example, the second distinct portionof the hybrid neural net predicts the geolocation of a plurality ofvehicles within the target region. To facilitate execution of therecharging plan, the parameter may impact an allocation of vehicles toat least a portion of recharging infrastructure within the predictedgeographic region.

In embodiments, vehicles described herein may comprise a system forautomating at least one control parameter of the vehicle. The vehiclesmay further at least operate as a semi-autonomous vehicle. The vehiclesmay be automatically routed. Also, the vehicles, recharging andotherwise may be self-driving vehicles.

In embodiments, leveraging network technologies for a transportationsystem may support a cognitive collective charging or refueling plan forvehicles in the transportation system. Such a transportation system mayinclude a cloud-based artificial intelligence system for taking inputsrelating to a plurality of vehicles, such as self-driving vehicles anddetermining at least one parameter of a re-charging and/or refuelingplan for at least one of the plurality of vehicles based on the inputs.

In embodiments, such a vehicle transportation system may include acloud-enabled vehicle information ingestion port 4732 that may provide anetwork (e.g., Internet and the like) interface through which inputs,such as inputs comprising operational state and energy consumptioninformation from at least one of a plurality of network-enabled vehicles4710 may be gathered and provided into cloud resources, such as thecloud-based control and artificial intelligence systems describedherein. In embodiments, such inputs may be gathered in real time as aplurality of vehicles 4710 connect to the cloud and deliver vehicleoperational state, energy consumption and other related informationthrough at least the port 4732. In embodiments, the inputs may relate tovehicle energy consumption and may be determined from a battery chargestate of a portion of the plurality of vehicles. The inputs may includea route plan for the vehicle, an indicator of the value of charging ofthe vehicle, and the like. The inputs may include predicted trafficconditions for the plurality of vehicles. The transportation system mayalso include vehicle charging or refueling infrastructure that mayinclude one or more vehicle charging infrastructure cloud-based controlsystem(s) 4734. These cloud-based control system(s) 4734 may receive theoperational state and energy consumption information for the pluralityof network-enabled vehicles 4710 via the cloud-enabled ingestion port4732 and/or directly through a common or set of connected networks, suchas the Internet and the like. Such a transportation system may furtherinclude a cloud-based artificial intelligence system 4736 that may befunctionally connected with the vehicle charging infrastructurecloud-based control system(s) 4734 that, for example may determine,provide, adjust or create at least one charging plan parameter 4714 uponwhich a charging plan 4712 for at least a portion of the plurality ofnetwork-enabled vehicles 4710 is dependent. This dependency may yieldchanges in the application of the charging plan 4712 by the cloud-basedcontrol system(s) 4734, such as when a processor of the cloud-basedcontrol system(s) 4734 executes a program derived from or based on thecharging plan 4712. The charging infrastructure cloud-based controlsystem(s) 4734 may include a cloud-based computing system remote fromcharging infrastructure systems (e.g., remote from an electric vehiclecharging kiosk and the like); it may also include a local charginginfrastructure system 4738 that may be disposed with and/or integratedinto an infrastructure element, such as a fuel station, a charging kioskand the like. In embodiments, the cloud-based artificial intelligencesystem 4736 may interface and coordinate with the cloud-based charginginfrastructure control system 4734, the local charging infrastructuresystem 4738 or both. In embodiments, coordination of the cloud-basedsystem may take on a form of interfacing, such as providing parametersthat affect more than one charging kiosk and the like than may bedifferent from coordination with the local charging infrastructuresystem 4738, which may provide information that the local system coulduse to adapt cloud-based charging system control commands and the likethat may be provided from, for example, a cloud-based control system4734. In an example, a cloud-based control system (that may control onlya portion, such as a localized set, of available charging or refuelinginfrastructure devices) may respond to the charging plan parameter 4714of the cloud-based artificial intelligence system 4736 by setting acharging rate that facilitates highly parallel vehicle charging.However, the local charging infrastructure system 4738 may adapt thiscontrol plan, such as based on a control plan parameter provided to itby the cloud-based artificial intelligence system 4736, to permit adifferent charging rate (e.g., a faster charging rate), such as for abrief period to accommodate an accumulation of vehicles queued up orestimated to use a local charging kiosk in the period. In this way, anadjustment to the at least one parameter 4714 that when made to thecharge infrastructure operation plan 4712 ensures that the at least oneof the plurality of vehicles 4710 has access to energy renewal in atarget energy renewal region 4716.

In embodiments, a charging or refueling plan may have a plurality ofparameters that may impact a wide range of transportation aspectsranging from vehicle-specific to vehicle group-specific to vehiclelocation-specific and infrastructure impacting aspects. Therefore, aparameter of the plan may impact or relate to any of vehicle routing tocharging infrastructure, amount of charge permitted to be provided,duration of time or rate for charging, battery conditions or state,battery charging profile, time required to charge to a minimum valuethat may be based on consumption needs of the vehicle(s), market valueof charging, indicators of market value, market price, infrastructureprovider profit, bids or offers for providing fuel or electricity to oneor more charging or refueling infrastructure kiosks, available supplycapacity, recharge demand (local, regional, system wide), and the like.

In embodiments, to facilitate a cognitive charging or refueling plan,the transportation system may include a recharging plan update facilitythat interacts with the cloud-based artificial intelligence system 4736to apply an adjustment value 4724 to the at least one of the pluralityof recharging plan parameters 4714. An adjustment value 4724 may befurther adjusted based on feedback of applying the adjustment value. Inembodiments, the feedback may be used by the cloud-based artificialintelligence system 4734 to further adjust the adjustment value. In anexample, feedback may impact the adjustment value applied to charging orrefueling infrastructure facilities in a localized way, such as for atarget recharging area 4716 or geographic range relative to one or morevehicles. In embodiments, providing a parameter adjustment value mayfacilitate optimizing consumption of a remaining battery charge state ofat least one of the plurality of vehicles.

By processing energy-related consumption, demand, availability, andaccess information and the like, the cloud-based artificial intelligencesystem 4736 may optimize aspects of the transportation system, such asvehicle electricity usage. The cloud-based artificial intelligencesystem 4736 may further optimize at least one of recharging time,location, and amount. In an example, a recharging plan parameter thatmay be configured and updated based on feedback may be a routingparameter for the at least one of the plurality of vehicles.

The cloud-based artificial intelligence system 4736 may further optimizea transportation system charging or refueling control plan parameter4714 to, for example, accommodate near-term charging needs for theplurality of rechargeable vehicles 4710 based on the optimized at leastone parameter. The cloud-based artificial intelligence system 4736 mayexecute an optimizing algorithm that may calculate energy parameters(including vehicle and non-vehicle energy), optimizes electricity usagefor at least vehicles and/or charging or refueling infrastructure, andoptimizes at least one charging or refueling infrastructure-specificrecharging time, location, and amount.

In embodiments, the cloud-based artificial intelligence system 4734 maypredict a geolocation 4718 of one or more vehicles within a geographicregion 4716. The geographic region 4716 may include vehicles that arecurrently located in or predicted to be in the region and optionally mayrequire or prefer recharging or refueling. As an example of predictinggeolocation and its impact on a charging plan, a charging plan parametermay include allocation of vehicles currently in or predicted to be inthe region to charging or refueling infrastructure in the geographicregion 4716. In embodiments, geolocation prediction may includereceiving inputs relating to charging states of a plurality of vehicleswithin or predicted to be within a geolocation range so that thecloud-based artificial intelligence system can optimize at least onecharging plan parameter 4714 based on a prediction of geolocations ofthe plurality of vehicles.

There are many aspects of a charging plan that may be impacted. Someaspects may be financial related, such as automated negotiation of atleast one of a duration, a quantity and a price for charging orrefueling a vehicle.

The transportation system cognitive charging plan system may include thecloud-based artificial intelligence system being configured with ahybrid neural network. A first neural network 4722 of the hybrid neuralnetwork may be used to process inputs relating to charge or fuel statesof the plurality of vehicles (directly received from the vehicles orthrough the vehicle information port 4732) and a second neural network4720 of the hybrid neural network is used to process inputs relating tocharging or refueling infrastructure and the like. In embodiments, thefirst neural network 4722 may process inputs comprising vehicle routeand stored energy state information for a plurality of vehicles topredict for at least one of the plurality of vehicles a target energyrenewal region. The second neural network 4720 may process vehicleenergy renewal infrastructure usage and demand information for vehicleenergy renewal infrastructure facilities within the target energyrenewal region to determine at least one parameter 4714 of a chargeinfrastructure operational plan 4712 that facilitates access by the atleast one of the plurality vehicles to renewal energy in the targetenergy renewal region 4716. In embodiments, the first and/or secondneural networks may be configured as any of the neural networksdescribed herein including without limitation convolutional typenetworks.

In embodiments, a transportation system may be distributed and mayinclude a cloud-based artificial intelligence system 4736 for takinginputs relating to a plurality of vehicles 4710 and determining at leastone parameter 4714 of a re-charging and refueling plan 4712 for at leastone of the plurality of vehicles based on the inputs. In embodiments,such inputs may be gathered in real time as plurality of vehicles 4710connect to and deliver vehicle operational state, energy consumption andother related information. In embodiments, the inputs may relate tovehicle energy consumption and may be determined from a battery chargestate of a portion of the plurality of vehicles. The inputs may includea route plan for the vehicle, an indicator of the value of charging ofthe vehicle, and the like. The inputs may include predicted trafficconditions for the plurality of vehicles. The distributed transportationsystem may also include cloud-based and vehicle-based systems thatexchange information about the vehicle, such as energy consumption andoperational information and information about the transportation system,such as recharging or refueling infrastructure. The cloud-basedartificial intelligence system may respond to transportation system andvehicle information shared by the cloud and vehicle-based system withcontrol parameters that facilitate executing a cognitive charging planfor at least a portion of charging or refueling infrastructure of thetransportation system. The cloud-based artificial intelligence system4736 may determine, provide, adjust or create at least one charging planparameter 4714 upon which a charging plan 4712 for at least a portion ofthe plurality of vehicles 4710 is dependent. This dependency may yieldchanges in the execution of the charging plan 4712 by at least one thecloud-based and vehicle-based systems, such as when a processor executesa program derived from or based on the charging plan 4712.

In embodiments, a cloud-based artificial intelligence system of atransportation system may facilitate execution of a cognitive chargingplan by applying a vehicle recharging facility utilization optimizationalgorithm to a plurality of rechargeable vehicle-specific inputs, e.g.,current operating state data for rechargeable vehicles present in atarget recharging range of one of the plurality of rechargeablevehicles. The cloud-based artificial intelligence system may alsoevaluate an impact of a plurality of recharging plan parameters onrecharging infrastructure of the transportation system in the targetrecharging range. The cloud-based artificial intelligence system mayselect at least one of the plurality of recharging plan parameters thatfacilitates, for example optimizing energy usage by the plurality ofrechargeable vehicles and generate an adjustment value for the at leastone of the plurality of recharging plan parameters. The cloud-basedartificial intelligence system may further predict a near-term need forrecharging for a portion of the plurality of rechargeable vehicleswithin the target region based on, for example operational status of theplurality of rechargeable vehicles that may be determined from therechargeable vehicle-specific inputs. Based on this prediction andnear-term recharging infrastructure availability and capacityinformation, the cloud-based artificial intelligence system may optimizeat least one parameter of the recharging plan. In embodiments, thecloud-based artificial intelligence system may operate a hybrid neuralnetwork for the predicting and parameter selection or adjustment. In anexample, a first portion of the hybrid neural network may process inputsthat relates to route plans for one more rechargeable vehicles. In theexample, a second portion of the hybrid neural network that is distinctfrom the first portion may process inputs relating to recharginginfrastructure within a recharging range of at least one of therechargeable vehicles. In this example, the second distinct portion ofthe hybrid neural net predicts the geolocation of a plurality ofvehicles within the target region. To facilitate execution of therecharging plan, the parameter may impact an allocation of vehicles toat least a portion of recharging infrastructure within the predictedgeographic region.

In embodiments, vehicles described herein may comprise a system forautomating at least one control parameter of the vehicle. The vehiclesmay further at least operate as a semi-autonomous vehicle. The vehiclesmay be automatically routed. Also, the vehicles, recharging andotherwise may be self-driving vehicles.

Referring to FIG. 48 , provided herein are transportation systems 4811having a robotic process automation system 48181 (RPA system). Inembodiments, data is captured for each of a set of individuals/users4891 as the individuals/users 4890 interact with a user interface 4823of a vehicle 4811, and an artificial intelligence system 4836 is trainedusing the data and interacts with the vehicle 4810 to automaticallyundertake actions with the vehicle 4810 on behalf of the user 4890. Data48114 collected for the RPA system 48181 may include a sequence ofimages, sensor data, telemetry data, or the like, among many other typesof data described throughout this disclosure. Interactions of anindividual/user 4890 with a vehicle 4810 may include interactions withvarious vehicle interfaces as described throughout this disclosure. Forexample, a robotic process automation (RPA) system 4810 may observepatterns of a driver, such as braking patterns, typical followingdistance behind other vehicles, approach to curves (e.g., entry angle,entry speed, exit angle, exit speed and the like), accelerationpatterns, lane preferences, passing preferences, and the like. Suchpatterns may be obtained through vision systems 48186 (e.g., onesobserving the driver, the steering wheel, the brake, the surroundingenvironment 48171, and the like), through vehicle data systems 48185(e.g., data streams indicating states and changes in state in steering,braking and the like, as well as forward and rear-facing cameras andsensors), through connected systems 48187 (e.g., GPS, cellular systems,and other network systems, as well as peer-to-peer, vehicle-to-vehicle,mesh and cognitive networks, among others), and other sources. Using atraining data set, the RPA system 48181, such as via a neural network48108 of any of the types described herein, may learn to drive in thesame style as a driver. In embodiments, the RPA system 48181 may learnchanges in style, such as varying levels of aggressiveness in differentsituations, such as based on time of day, length of trip, type of trip,or the like. Thus, a self-driving car may learn to drive like itstypical driver. Similarly, an RPA system 48181 may be used to observedriver, passenger, or other individual interactions with a navigationsystem, an audio entertainment system, a video entertainment system, aclimate control system, a seat warming and/or cooling system, a steeringsystem, a braking system, a mirror system, a window system, a doorsystem, a trunk system, a fueling system, a moonroof system, aventilation system, a lumbar support system, a seat positioning system,a GPS system, a WIFI system, a glovebox system, or other system.

An aspect provided herein includes a system 4811 for transportation,comprising: a robotic process automation system 48181. In embodiments, aset of data is captured for each user 4890 in a set of users 4891 aseach user 4890 interacts with a user interface 4823 of a vehicle 4810.In embodiments, an artificial intelligence system 4836 is trained usingthe set of data 48114 to interact with the vehicle 4810 to automaticallyundertake actions with the vehicle 4810 on behalf of the user 4890.

FIG. 49 illustrates a method 4900 of robotic process automation tofacilitate mimicking human operator operation of a vehicle in accordancewith embodiments of the systems and methods disclosed herein. At 4902the method includes tracking human interactions with a vehiclecontrol-facilitating interface. At 4904 the method includes recordingthe tracked human interactions in a robotic process automation systemtraining data structure. At 4906 the method includes tracking vehicleoperational state information of the vehicle. In embodiments, thevehicle is to be controlled through the vehicle control-facilitatinginterface. At 4908 the method includes recording the vehicle operationalstate information in the robotic process automation system training datastructure. At 4909 the method includes training, through the use of atleast one neural network, an artificial intelligence system to operatethe vehicle in a manner consistent with the human interactions based onthe human interactions and the vehicle operational state information inthe robotic process automation system training data structure.

In embodiments, the method further comprises controlling at least oneaspect of the vehicle with the trained artificial intelligence system.In embodiments, the method further comprises applying deep learning tothe controlling the at least one aspect of the vehicle by structuredvariation in the controlling the at least one aspect of the vehicle tomimic the human interactions and processing feedback from thecontrolling the at least one aspect of the vehicle with machinelearning. In embodiments, the controlling at least one aspect of thevehicle is performed via the vehicle control-facilitating interface.

In embodiments, the controlling at least one aspect of the vehicle isperformed by the artificial intelligence system emulating thecontrol-facilitating interface being operated by the human. Inembodiments, the vehicle control-facilitating interface comprises atleast one of an audio capture system to capture audible expressions ofthe human, a human-machine interface, a mechanical interface, an opticalinterface and a sensor-based interface. In embodiments, the trackingvehicle operational state information comprises tracking at least one ofa set of vehicle systems and a set of vehicle operational processesaffected by the human interactions. In embodiments, the tracking vehicleoperational state information comprises tracking at least one vehiclesystem element. In embodiments, the at least one vehicle system elementis controlled via the vehicle control-facilitating interface. Inembodiments, the at least one vehicle system element is affected by thehuman interactions. In embodiments, the tracking vehicle operationalstate information comprises tracking the vehicle operational stateinformation before, during, and after the human interaction.

In embodiments, the tracking vehicle operational state informationcomprises tracking at least one of a plurality of vehicle control systemoutputs that result from the human interactions and vehicle operationalresults achieved in response to the human interactions. In embodiments,the vehicle is to be controlled to achieve results that are consistentwith results achieved via the human interactions. In embodiments, themethod further comprises tracking and recording conditions proximal tothe vehicle with a plurality of vehicle mounted sensors. In embodiments,the training of the artificial intelligence system is further responsiveto the conditions proximal to the vehicle tracked contemporaneously tothe human interactions. In embodiments, the training is furtherresponsive to a plurality of data feeds from remote sensors, theplurality of data feeds comprising data collected by the remove sensorscontemporaneous to the human interactions. In embodiments, theartificial intelligence system employs a workflow that involvesdecision-making and the robotic process automation system facilitatesautomation of the decision-making. In embodiments, the artificialintelligence system employs a workflow that involves remote control ofthe vehicle and the robotic process automation system facilitatesautomation of remotely controlling the vehicle.

An aspect provided herein includes a transportation system 4811 formimicking human operation of a vehicle 4810, comprising: a roboticprocess automation system 48181 comprising: an operator data collectionmodule 48182 to capture human operator interaction with a vehiclecontrol system interface 48191; a vehicle data collection module 48183to capture vehicle response and operating conditions associated at leastcontemporaneously with the human operator interaction; and anenvironment data collection module 48184 to capture instances ofenvironmental information associated at least contemporaneously with thehuman operator interaction; and an artificial intelligence system 4836to learn to mimic the human operator (e.g., user 4890) to control thevehicle 4810 responsive to the robotic process automation system 48181detecting data 48114 indicative of at least one of a plurality of theinstances of environmental information associated with thecontemporaneously captured vehicle response and operating conditions.

In embodiments, the operator data collection module 48182 is to capturepatterns of data including braking patterns, follow-behind distance,approach to curve acceleration patterns, lane preferences, and passingpreferences. In embodiments, vehicle data collection module 48183captures data from a plurality of vehicle data systems 48185 thatprovide data streams indicating states and changes in state in steering,braking, acceleration, forward looking images, and rear-looking images.In embodiments, the artificial intelligence system 4836 includes aneural network 48108 for training the artificial intelligence system4836.

FIG. 50 illustrates a robotic process automation method 5000 ofmimicking human operation of a vehicle in accordance with embodiments ofthe systems and methods disclosed herein. At 5002 the method includescapturing human operator interactions with a vehicle control systeminterface. At 5004 the method includes capturing vehicle response andoperating conditions associated at least contemporaneously with thehuman operator interaction. At 5006 the method includes capturinginstances of environmental information associated at leastcontemporaneously with the human operator interaction. At 5008 themethod includes training an artificial intelligence system to controlthe vehicle mimicking the human operator responsive to the environmentdata collection module detecting data indicative of at least one of aplurality of the instances of environmental information associated withthe contemporaneously captured vehicle response and operatingconditions.

In embodiments, the method further comprises applying deep learning inthe artificial intelligence system to optimize a margin of vehicleoperating safety by affecting the controlling of the at least one aspectof the vehicle by structured variation in the controlling of the atleast one aspect of the vehicle to mimic the human interactions andprocessing feedback from the controlling the at least one aspect of thevehicle with machine learning. In embodiments, the robotic processautomation system facilitates automation of a decision-making workflowemployed by the artificial intelligence system. In embodiments, therobotic process automation system facilitates automation of a remotecontrol workflow that the artificial intelligence system employs toremotely control the vehicle.

Referring to FIG. 51 , a transportation system 5111 is provided havingan artificial intelligence system 5136 that automatically randomizes aparameter of an in-vehicle experience in order to improve a user statethat benefits from variation. In embodiments, a system used to control adriver or passenger experience (such as in a self-driving car, assistedcar, or conventional vehicle) may be configured to automaticallyundertake actions based on an objective or feedback function, such aswhere an artificial intelligence system 5136 is trained on outcomes froma training data set to provide outputs to one or more vehicle systems toimprove health, satisfaction, mood, safety, one or more financialmetrics, efficiency, or the like.

Such systems may involve a wide range of in-vehicle experienceparameters (including any of the experience parameters described herein,such as driving experience (including assisted and self-driving, as wellas vehicle responsiveness to inputs, such as in controlled suspensionperformance, approaches to curves, braking and the like), seatpositioning (including lumbar support, leg room, seatback angle, seatheight and angle, etc.), climate control (including ventilation, windowor moonroof state (e.g., open or closed), temperature, humidity, fanspeed, air motion and the like), sound (e.g., volume, bass, treble,individual speaker control, focus area of sound, etc.), content (audio,video and other types, including music, news, advertising and the like),route selection (e.g., for speed, for road experience (e.g., smooth orrough, flat or hilly, straight or curving), for points of interest(POIs), for view (e.g., scenic routes), for novelty (e.g., to seedifferent locations), and/or for defined purposes (e.g., shoppingopportunities, saving fuel, refueling opportunities, rechargingopportunities, or the like).

In many situations, variation of one or more vehicle experienceparameters may provide or result in a preferred state for a vehicle 5110(or set of vehicles), a user (such as vehicle rider 51120), or both, ascompared to seeking to find a single optimized state of such aparameter. For example, while a user may have a preferred seat position,sitting in the same position every day, or during an extended period onthe same day, may have adverse effects, such as placing undue pressureon certain joints, promoting atrophy of certain muscles, diminishingflexibility of soft tissue, or the like. In such a situation, anautomated control system (including one that is configured to useartificial intelligence of any of the types described herein) may beconfigured to induce variation in one or more of the user experienceparameters described herein, optionally with random variation or withvariation that is according to a prescribed pattern, such as one thatmay be prescribed according to a regimen, such as one developed toprovide physical therapy, chiropractic, or other medical or healthbenefits. As one example, seat positioning may be varied over time topromote health of joints, muscles, ligaments, cartilage or the like. Asanother example, consistent with evidence that human health is improvedwhen an individual experiences significant variations in temperature,humidity, and other climate factors, a climate control system may bevaried (randomly or according to a defined regimen) to provide varyingtemperature, humidity, fresh air (including by opening windows orventilation) or the like in order to improve the health, mood, oralertness of a user.

An artificial intelligence-based control system 5136 may be trained on aset of outcomes (of various types described herein) to provide a levelof variation of a user experience that achieves desired outcomes,including selection of the timing and extent of such variations. Asanother example, an audio system may be varied to preserve hearing (suchas based on tracking accumulated sound pressure levels, accumulateddosage, or the like), to promote alertness (such as by varying the typeof content), and/or to improve health (such as by providing a mix ofstimulating and relaxing content). In embodiments, such an artificialintelligence system 5136 may be fed sensor data 51444, such as from awearable device 51157 (including a sensor set) or a physiologicalsensing system 51190, which includes a set of systems and/or sensorscapable of providing physiological monitoring within a vehicle 5110(e.g., a vison-based system 51186 that observes a user, a sensor 5125embedded in a seat, a steering wheel, or the like that can measure aphysiological parameter, or the like). For example, a vehicle interface51188 (such as a steering wheel or any other interface described herein)can measure a physiological parameter (e.g., galvanic skin response,such as to indicate a stress level, cortisol level, or the like of adriver or other user), which can be used to indicate a current state forpurposes of control or can be used as part of a training data set tooptimize one or more parameters that may benefit from control, includingcontrol of variation of user experience to achieve desired outcomes. Inone such example, an artificial intelligence system 5136 may varyparameters, such as driving experience, music and the like, to accountfor changes in hormonal systems of the user (such as cortisol and otheradrenal system hormones), such as to induce healthy changes in state(consistent with evidence that varying cortisol levels over the courseof a day are typical in healthy individuals, but excessively high or lowlevels at certain times of day may be unhealthy or unsafe). Such asystem may, for example, “amp up” the experience with more aggressivesettings (e.g., more acceleration into curves, tighter suspension,and/or louder music) in the morning when rising cortisol levels arehealthy and “mellow out” the experience (such as by softer suspension,relaxing music and/or gentle driving motion) in the afternoon whencortisol levels should be dropping to lower levels to promote health.Experiences may consider both health of the user and safety, such as byensuring that levels vary over time, but are sufficiently high to assurealertness (and hence safety) in situations where high alertness isrequired. While cortisol (an important hormone) is provided as anexample, user experience parameters may be controlled (optionally withrandom or configured variation) with respect to other hormonal orbiological systems, including insulin-related systems, cardiovascularsystems (e.g., relating to pulse and blood pressure), gastrointestinalsystems, and many others.

An aspect provided herein includes a system for transportation 5111,comprising: an artificial intelligence system 5136 to automaticallyrandomize a parameter of an in-vehicle experience to improve a userstate. In embodiments, the user state benefits from variation of theparameter.

An aspect provided herein includes a system for transportation 5111,comprising: a vehicle interface 51188 for gathering physiological senseddata of a rider 51120 in the vehicle 5110; and an artificialintelligence-based circuit 51189 that is trained on a set of outcomesrelated to rider in-vehicle experience and that induces, responsive tothe sensed rider physiological data, variation in one or more of theuser experience parameters to achieve at least one desired outcome inthe set of outcomes, the inducing variation including control of timingand extent of the variation.

In embodiments, the induced variation includes random variation. Inembodiments, the induced variation includes variation that is accordingto a prescribed pattern. In embodiments, the prescribed pattern isprescribed according to a regimen. In embodiments, the regimen isdeveloped to provide at least one of physical therapy, chiropractic, andother medical health benefits. In embodiments, the one or more userexperience parameters affect at least one of seat position, temperature,humidity, cabin air source, or audio output. In embodiments, the vehicleinterface 51188 comprises at least one wearable sensor 51157 disposed tobe worn by the rider 51120. In embodiments, the vehicle interface 51188comprises a vision system 51186 disposed to capture and analyze imagesfrom a plurality of perspectives of the rider 51120. In embodiments, thevariation in one or more of the user experience parameters comprisesvariation in control of the vehicle 5110.

In embodiments, variation in control of the vehicle 5110 includesconfiguring the vehicle 5110 for aggressive driving performance. Inembodiments, variation in control of the vehicle 5110 includesconfiguring the vehicle 5110 for non-aggressive driving performance. Inembodiments, the variation is responsive to the physiological senseddata that includes an indication of a hormonal level of the rider 51120,and the artificial intelligence-based circuit 51189 varies the one ormore user experience parameters to promote a hormonal state thatpromotes rider safety.

Referring now to FIG. 52 , also provided herein are transportationsystems 5211 having a system 52192 for taking an indicator of a hormonalsystem level of a user 5290 and automatically varying a user experiencein the vehicle 5210 to promote a hormonal state that promotes safety.

An aspect provided herein includes a system for transportation 5211,comprising: a system 52192 for detecting an indicator of a hormonalsystem level of a user 5290 and automatically varying a user experiencein a vehicle 5210 to promote a hormonal state that promotes safety.

An aspect provided herein includes a system for transportation 5211comprising: a vehicle interface 52188 for gathering hormonal state dataof a rider (e.g., user 5290) in the vehicle 5210; and an artificialintelligence-based circuit 52189 that is trained on a set of outcomesrelated to rider in-vehicle experience and that induces, responsive tothe sensed rider hormonal state data, variation in one or more of theuser experience parameters to achieve at least one desired outcome inthe set of outcomes, the set of outcomes including a least one outcomethat promotes rider safety, the inducing variation including control oftiming and extent of the variation.

In embodiments, the variation in the one or more user experienceparameters is controlled by the artificial intelligence system 5236 topromote a desired hormonal state of the rider (e.g., user 5290). Inembodiments, the desired hormonal state of the rider promotes safety. Inembodiments, the at least one desired outcome in the set of outcomes isthe at least one outcome that promotes rider safety. In embodiments, thevariation in the one or more user experience parameters includes varyingat least one of a food and a beverage offered to the rider (e.g., user5290). In embodiments, the one or more user experience parameters affectat least one of seat position, temperature, humidity, cabin air source,or audio output. In embodiments, the vehicle interface 52188 comprisesat least one wearable sensor 52157 disposed to be worn by the rider(e.g., user 5290).

In embodiments, the vehicle interface 52188 comprises a vision system52186 disposed to capture and analyze images from a plurality ofperspectives of the rider (e.g., user 5290). In embodiments, thevariation in one or more of the user experience parameters comprisesvariation in control of the vehicle 5210. In embodiments, variation incontrol of the vehicle 5210 includes configuring the vehicle 5210 foraggressive driving performance. In embodiments, variation in control ofthe vehicle 5210 includes configuring the vehicle 5210 fornon-aggressive driving performance.

Referring to FIG. 53 , provided herein are transportation systems 5311having a system for optimizing at least one of a vehicle parameter 53159and a user experience parameter 53205 to provide a margin of safety53204. In embodiments, the margin of safety 53204 may be a user-selectedmargin of safety or user-based margin of safety, such as selected basedon a profile of a user or actively selected by a user, such as byinteraction with a user interface, or selected based on a profiledeveloped by tracking user behavior, including behavior in a vehicle5310 and in other contexts, such as on social media, in e-commerce, inconsuming content, in moving from place-to-place, or the like. In manysituations, there is a tradeoff between optimizing the performance of adynamic system (such as to achieve some objective function, like fuelefficiency) and one or more risks that are present in the system. Thisis particularly true in situations where there is some asymmetry betweenthe benefits of optimizing one or more parameters and the risks that arepresent in the dynamic systems in which the parameter plays a role. Asan example, seeking to minimize travel time (such as for a dailycommute), leads to an increased likelihood of arriving late, because awide range of effects in dynamic systems, such as ones involving vehicletraffic, tend to cascade and periodically produce travel times that varywidely (and quite often adversely). Variances in many systems are notsymmetrical; for example, unusually uncrowded roads may improve a30-mile commute time by a few minutes, but an accident, or highcongestion, can increase the same commute by an hour or more. Thus, toavoid risks that have high adverse consequences, a wide margin of safetymay be required. In embodiments, systems are provided herein for usingan expert system (which may be model-based, rule-based, deep learning, ahybrid, or other intelligent systems as described herein) to provide adesired margin of safety with respect to adverse events that are presentin transportation-related dynamic systems. The margin of safety 53204may be provided via an output of the expert system 5336, such as aninstruction, a control parameter for a vehicle 5310 or an in-vehicleuser experience, or the like. An artificial intelligence system 5336 maybe trained to provide the margin of safety 53204 based on a training setof data based on outcomes of transportation systems, such as trafficdata, weather data, accident data, vehicle maintenance data, fueling andcharging system data (including in-vehicle data and data frominfrastructure systems, such as charging stations, fueling stations, andenergy production, transportation, and storage systems), user behaviordata, user health data, user satisfaction data, financial information(e.g., user financial information, pricing information (e.g., for fuel,for food, for accommodations along a route, and the like), vehiclesafety data, failure mode data, vehicle information system data, and thelike), and many other types of data as described herein and in thedocuments incorporated by reference herein.

An aspect provided herein includes a system for transportation 5311,comprising: a system for optimizing at least one of a vehicle parameter53159 and a user experience parameter 53205 to provide a margin ofsafety 53204.

An aspect provided herein includes a transportation system 5311 foroptimizing a margin of safety when mimicking human operation of avehicle 5310, the transportation system 5311 comprising: a set ofrobotic process automation systems 53181 comprising: an operator datacollection module 53182 to capture human operator 5390 interactions53201 with a vehicle control system interface 53191; a vehicle datacollection module 53183 to capture vehicle response and operatingconditions associated at least contemporaneously with the human operatorinteraction 53201; an environment data collection module 53184 tocapture instances of environmental information 53203 associated at leastcontemporaneously with the human operator interactions 53201; and anartificial intelligence system 5336 to learn to control the vehicle 5310with an optimized margin of safety while mimicking the human operator.In embodiments, the artificial intelligence system 5336 is responsive tothe robotic process automation system 53181. In embodiments, theartificial intelligence system 5336 is to detect data indicative of atleast one of a plurality of the instances of environmental informationassociated with the contemporaneously captured vehicle response andoperating conditions. In embodiments, the optimized margin of safety isto be achieved by training the artificial intelligence system 5336 tocontrol the vehicle 5310 based on a set of human operator interactiondata collected from interactions of a set of expert human vehicleoperators with the vehicle control system interface 53191.

In embodiments, the operator data collection module 53182 capturespatterns of data including braking patterns, follow-behind distance,approach to curve acceleration patterns, lane preferences, or passingpreferences. In embodiments, the vehicle data collection module 53183captures data from a plurality of vehicle data systems that provide datastreams indicating states and changes in state in steering, braking,acceleration, forward looking images, or rear-looking images. Inembodiments, the artificial intelligence system includes a neuralnetwork 53108 for training the artificial intelligence system 53114.

FIG. 54 illustrates a method 5400 of robotic process automation forachieving an optimized margin of vehicle operational safety inaccordance with embodiments of the systems and methods disclosed herein.At 5402 the method includes tracking expert vehicle control humaninteractions with a vehicle control-facilitating interface. At 5404 themethod includes recording the tracked expert vehicle control humaninteractions in a robotic process automation system training datastructure. At 5406 the method includes tracking vehicle operationalstate information of a vehicle. At 5407 the method includes recordingvehicle operational state information in the robotic process automationsystem training data structure. At 5408 the method includes training,via at least one neural network, the vehicle to operate with anoptimized margin of vehicle operational safety in a manner consistentwith the expert vehicle control human interactions based on the expertvehicle control human interactions and the vehicle operational stateinformation in the robotic process automation system training datastructure. At 5409 the method includes controlling at least one aspectof the vehicle with the trained artificial intelligence system.

Referring to FIG. 53 and FIG. 54 , in embodiments, the method furthercomprises applying deep learning to optimize the margin of vehicleoperational safety by controlling the at least one aspect of the vehiclethrough structured variation in the controlling the at least one aspectof the vehicle to mimic the expert vehicle control human interactions53201 and processing feedback from the controlling the at least oneaspect of the vehicle with machine learning. In embodiments, thecontrolling at least one aspect of the vehicle is performed via thevehicle control-facilitating interface 53191. In embodiments, thecontrolling at least one aspect of the vehicle is performed by theartificial intelligence system emulating the control-facilitatinginterface being operated by the expert vehicle control human 53202. Inembodiments, the vehicle control-facilitating interface 53191 comprisesat least one of an audio capture system to capture audible expressionsof the expert vehicle control human, a human-machine interface,mechanical interface, an optical interface and a sensor-based interface.In embodiments, the tracking vehicle operational state informationcomprises tracking at least one of vehicle systems and vehicleoperational processes affected by the expert vehicle control humaninteractions. In embodiments, the tracking vehicle operational stateinformation comprises tracking at least one vehicle system element. Inembodiments, the at least one vehicle system element is controlled viathe vehicle control-facilitating interface. In embodiments, the at leastone vehicle system element is affected by the expert vehicle controlhuman interactions.

In embodiments, the tracking vehicle operational state informationcomprises tracking the vehicle operational state information before,during, and after the expert vehicle control human interaction. Inembodiments, the tracking vehicle operational state informationcomprises tracking at least one of a plurality of vehicle control systemoutputs that result from the expert vehicle control human interactionsand vehicle operational results achieved responsive to the expertvehicle control human interactions. In embodiments, the vehicle is to becontrolled to achieve results that are consistent with results achievedvia the expert vehicle control human interactions.

In embodiments, the method further comprises tracking and recordingconditions proximal to the vehicle with a plurality of vehicle mountedsensors. In embodiments, the training of the artificial intelligencesystem is further responsive to the conditions proximal to the vehicletracked contemporaneously to the expert vehicle control humaninteractions. In embodiments, the training is further responsive to aplurality of data feeds from remote sensors, the plurality of data feedscomprising data collected by the remote sensors contemporaneous to theexpert vehicle control human interactions.

FIG. 55 illustrates a method 5500 for mimicking human operation of avehicle by robotic process automation of in accordance with embodimentsof the systems and methods disclosed herein. At 5502 the method includescapturing human operator interactions with a vehicle control systeminterface operatively connected to a vehicle. At 5504 the methodincludes capturing vehicle response and operating conditions associatedat least contemporaneously with the human operator interaction. At 5506the method includes capturing environmental information associated atleast contemporaneously with the human operator interaction. At 5508 themethod includes training an artificial intelligence system to controlthe vehicle with an optimized margin of safety while mimicking the humanoperator, the artificial intelligence system taking input from theenvironment data collection module about the instances of environmentalinformation associated with the contemporaneously collected vehicleresponse and operating conditions. In embodiments, the optimized marginof safety is achieved by training the artificial intelligence system tocontrol the vehicle based on a set of human operator interaction datacollected from interactions of an expert human vehicle operator and aset of outcome data from a set of vehicle safety events.

Referring to FIGS. 53 and 55 in embodiments, the method furthercomprises: applying deep learning of the artificial intelligence system53114 to optimize a margin of vehicle operating safety 53204 byaffecting a controlling of at least one aspect of the vehicle throughstructured variation in control of the at least one aspect of thevehicle to mimic the expert vehicle control human interactions 53201 andprocessing feedback from the controlling of the at least one aspect ofthe vehicle with machine learning. In embodiments, the artificialintelligence system employs a workflow that involves decision-making andthe robotic process automation system 53181 facilitates automation ofthe decision-making. In embodiments, the artificial intelligence systememploys a workflow that involves remote control of the vehicle and therobotic process automation system facilitates automation of remotelycontrolling the vehicle 5310.

Referring now to FIG. 56 , a transportation system 5611 is depictedwhich includes an interface 56133 by which a set of expert systems 5657may be configured to provide respective outputs 56193 for managing atleast one of a set of vehicle parameters, a set of fleet parameters anda set of user experience parameters.

Such an interface 56133 may include a graphical user interface (such ashaving a set of visual elements, menu items, forms, and the like thatcan be manipulated to enable selection and/or configuration of an expertsystem 5657), an application programming interface, an interface to acomputing platform (e.g., a cloud-computing platform, such as toconfigure parameters of one or more services, programs, modules, or thelike), and others. For example, an interface 56133 may be used to selecta type of expert system 5657, such as a model (e.g., a selected modelfor representing behavior of a vehicle, a fleet or a user, or a modelrepresenting an aspect of an environment relevant to transportation,such as a weather model, a traffic model, a fuel consumption model, anenergy distribution model, a pricing model or the like), an artificialintelligence system (such as selecting a type of neural network, deeplearning system, or the like, of any type described herein), or acombination or hybrid thereof. For example, a user may, in an interface56133, elect to use the European Center for Medium-Range WeatherForecast (ECMWF) to forecast weather events that may impact atransportation environment, along with a recurrent neural network forforecasting user shopping behavior (such as to indicate likelypreferences of a user along a traffic route).

Thus, an interface 56133 may be configured to provide a host, manager,operator, service provider, vendor, or other entity interacting withinor with a transportation system 5611 with the ability to review a rangeof models, expert systems 5657, neural network categories, and the like.The interface 56133 may optionally be provided with one or moreindicators of suitability for a given purpose, such as one or moreratings, statistical measures of validity, or the like. The interface56133 may also be configured to select a set (e.g., a model, expertsystem, neural network, etc.) that is well adapted for purposes of agiven transportation system, environment, and purpose. In embodiments,such an interface 56133 may allow a user 5690 to configure one or moreparameters of an expert system 5657, such as one or more input datasources to which a model is to be applied and/or one or more inputs to aneural network, one or more output types, targets, durations, orpurposes, one or more weights within a model or an artificialintelligence system, one or more sets of nodes and/or interconnectionswithin a model, graph structure, neural network, or the like, one ormore time periods of input, output, or operation, one or morefrequencies of operation, calculation, or the like, one or more rules(such as rules applying to any of the parameters configured as describedherein or operating upon any of the inputs or outputs noted herein), oneor more infrastructure parameters (such as storage parameters, networkutilization parameters, processing parameters, processing platformparameters, or the like). As one example among many other possibleexample, a user 5690 may configure a selected neural network to takeinputs from a weather model, a traffic model, and a real-time trafficreporting system in order to provide a real-time output 56193 to arouting system for a vehicle 5610, where the neural network isconfigured to have ten million nodes and to undertake processing on aselected cloud platform.

In embodiments, the interface 56133 may include elements for selectionand/or configuration of a purpose, an objective or a desired outcome ofa system and/or sub-system, such as one that provides input, feedback,or supervision to a model, to a machine learning system, or the like.For example, a user 5690 may be allowed, in an interface 56133, toselect among modes (e.g., comfort mode, sports mode, high-efficiencymode, work mode, entertainment mode, sleep mode, relaxation mode,long-distance trip mode, or the like) that correspond to desiredoutcomes, which may include emotional outcomes, financial outcomes,performance outcomes, trip duration outcomes, energy utilizationoutcomes, environmental impact outcomes, traffic avoidance outcomes, orthe like. Outcomes may be declared with varying levels of specificity.Outcomes may be defined by or for a given user 5690 (such as based on auser profile or behavior) or for a group of users (such as by one ormore functions that harmonizes outcomes according to multiple userprofiles, such as by selecting a desired configuration that isconsistent with an acceptable state for each of a set of riders). As anexample, a rider may indicate a preferred outcome of activeentertainment, while another rider may indicate a preferred outcome ofmaximum safety. In such a case, the interface 56133 may provide a rewardparameter to a model or expert system 5657 for actions that reduce riskand for actions that increase entertainment, resulting in outcomes thatare consistent with objectives of both riders. Rewards may be weighted,such as to optimize a set of outcomes. Competition among potentiallyconflicting outcomes may be resolved by a model, by rule (e.g., avehicle owner's objectives may be weighted higher than other riders, aparent's over a child, or the like), or by machine learning, such as byusing genetic programming techniques (such as by varying combinations ofweights and/or outcomes randomly or systematically and determiningoverall satisfaction of a rider or set of riders).

An aspect provided herein includes a system for transportation 5611,comprising: an interface 56133 to configure a set of expert systems 5657to provide respective outputs 56193 for managing a set of parametersselected from the group consisting of a set of vehicle parameters, a setof fleet parameters, a set of user experience parameters, andcombinations thereof.

An aspect provided herein includes a system for configuration managementof components of a transportation system 5611 comprising: an interface56133 comprising: a first portion 56194 of the interface 56133 forconfiguring a first expert computing system of the expert computingsystems 5657 for managing a set of vehicle parameters; a second portion56195 of the interface 56133 for configuring a second expert computingsystem of the expert computing systems 5657 for managing a set ofvehicle fleet parameters; and a third portion 56196 of the interface56133 for configuring a third expert computing system for managing a setof user experience parameters. In embodiments, the interface 56133 is agraphical user interface through which a set of visual elements 56197presented in the graphical user interface, when manipulated in theinterface 56133 causes at least one of selection and configuration ofone or more of the first, second, and third expert systems 5657. Inembodiments, the interface 56133 is an application programminginterface. In embodiments, the interface 56133 is an interface to acloud-based computing platform through which one or moretransportation-centric services, programs and modules are configured.

An aspect provided herein includes a transportation system 5611comprising: an interface 56133 for configuring a set of expert systems5657 to provide outputs 56193 based on which the transportation system5611 manages transportation-related parameters. In embodiments, theparameters facilitate operation of at least one of a set of vehicles, afleet of vehicles, and a transportation system user experience; and aplurality of visual elements 56197 representing a set of attributes andparameters of the set of expert systems 5657 that are configurable bythe interface 56133 and a plurality of the transportation systems 5611.In embodiments, the interface 56133 is configured to facilitatemanipulating the visual elements 56197 thereby causing configuration ofthe set of expert systems 5657. In embodiments, the plurality of thetransportation systems comprise a set of vehicles 5610.

In embodiments, the plurality of the transportation systems comprise aset of infrastructure elements 56198 supporting a set of vehicles 5610.In embodiments, the set of infrastructure elements 56198 comprisesvehicle fueling elements. In embodiments, the set of infrastructureelements 56198 comprises vehicle charging elements. In embodiments, theset of infrastructure elements 56198 comprises traffic control lights.In embodiments, the set of infrastructure elements 56198 comprises atoll booth. In embodiments, the set of infrastructure elements 56198comprises a rail system. In embodiments, the set of infrastructureelements 56198 comprises automated parking facilities. In embodiments,the set of infrastructure elements 56198 comprises vehicle monitoringsensors.

In embodiments, the visual elements 56197 display a plurality of modelsthat can be selected for use in the set of expert systems 5657. Inembodiments, the visual elements 56197 display a plurality of neuralnetwork categories that can be selected for use in the set of expertsystems 5657. In embodiments, at least one of the plurality of neuralnetwork categories includes a convolutional neural network. Inembodiments, the visual elements 56197 include one or more indicators ofsuitability of items represented by the plurality of visual elements56197 for a given purpose. In embodiments, configuring a plurality ofexpert systems 5657 comprises facilitating selection sources of inputsfor use by at least a portion of the plurality of expert systems 5657.In embodiments, the interface 56133 facilitates selection, for at leasta portion of the plurality of expert systems 5657, one or more outputtypes, targets, durations, and purposes.

In embodiments, the interface 56133 facilitates selection, for at leasta portion of the plurality of expert systems 5657, of one or moreweights within a model or an artificial intelligence system. Inembodiments, the interface 56133 facilitates selection, for at least aportion of the plurality of expert systems 5657, of one or more sets ofnodes or interconnections within a model. In embodiments, the interface56133 facilitates selection, for at least a portion of the plurality ofexpert systems 5657, of a graph structure. In embodiments, the interface56133 facilitates selection, for at least a portion of the plurality ofexpert systems 5657, of a neural network. In embodiments, the interfacefacilitates selection, for at least a portion of the plurality of expertsystems, of one or more time periods of input, output, or operation.

In embodiments, the interface 56133 facilitates selection, for at leasta portion of the plurality of expert systems 5657, of one or morefrequencies of operation. In embodiments, the interface 56133facilitates selection, for at least a portion of the plurality of expertsystems 5657, of frequencies of calculation. In embodiments, theinterface 56133 facilitates selection, for at least a portion of theplurality of expert systems 5657, of one or more rules for applying tothe plurality of parameters. In embodiments, the interface 56133facilitates selection, for at least a portion of the plurality of expertsystems 5657, of one or more rules for operating upon any of the inputsor upon the provided outputs. In embodiments, the plurality ofparameters comprise one or more infrastructure parameters selected fromthe group consisting of storage parameters, network utilizationparameters, processing parameters, and processing platform parameters.

In embodiments, the interface 56133 facilitates selecting a class of anartificial intelligence computing system, a source of inputs to theselected artificial intelligence computing system, a computing capacityof the selected artificial intelligence computing system, a processorfor executing the artificial intelligence computing system, and anoutcome objective of executing the artificial intelligence computingsystem. In embodiments, the interface 56133 facilitates selecting one ormore operational modes of at least one of the vehicles 5610 in thetransportation system 5611. In embodiments, the interface 56133facilitates selecting a degree of specificity for outputs 56193 producedby at least one of the plurality of expert systems 5657.

Referring now to FIG. 57 , an example of a transportation system 5711 isdepicted having an expert system 5757 for configuring a recommendationfor a configuration of a vehicle 5710. In embodiments, therecommendation includes at least one parameter of configuration for theexpert system 5757 that controls a parameter of at least one of avehicle parameter 57159 and a user experience parameter 57205. Such arecommendation system may recommend a configuration for a user based ona wide range of information, including data sets indicating degrees ofsatisfaction of other users, such as user profiles, user behaviortracking (within a vehicle and outside), content recommendation systems(such as collaborative filtering systems used to recommend music,movies, video and other content), content search systems (e.g., such asused to provide relevant search results to queries), e-commerce trackingsystems (such as to indicate user preferences, interests, and intents),and many others. The recommendation system 57199 may use the foregoingto profile a rider and, based on indicators of satisfaction by otherriders, determine a configuration of a vehicle 5710, or an experiencewithin the vehicle 5710, for the rider.

The configuration may use similarity (such as by similarity matrixapproaches, attribute-based clustering approaches (e.g., k-meansclustering) or other techniques to group a rider with other similarriders. Configuration may use collaborative filtering, such as byquerying a rider about particular content, experiences, and the like andtaking input as to whether they are favorable or unfavorable (optionallywith a degree of favorability, such as a rating system (e.g., 5 starsfor a great item of content). The recommendation system 57199 may usegenetic programming, such as by configuring (with random and/orsystematic variation) combinations of vehicle parameters and/or userexperience parameters and taking inputs from a rider or a set of riders(e.g., a large survey group) to determine a set of favorableconfigurations. This may occur with machine learning over a large set ofoutcomes, where outcomes may include various reward functions of thetype described herein, including indicators of overall satisfactionand/or indicators of specific objectives. Thus, a machine learningsystem or other expert systems 5757 may learn to configure the overallride for a rider or set of riders and to recommend such a configurationfor a rider. Recommendations may be based on context, such as whether arider is alone or in a group, the time of day (or week, month or year),the type of trip, the objective of the trip, the type or road, theduration of a trip, the route, and the like.

An aspect provided herein includes a system for transportation 5711,comprising: an expert system 5757 to configure a recommendation for avehicle configuration. In embodiments, the recommendation includes atleast one parameter of configuration for the expert system 5757 thatcontrols a parameter selected from the group consisting of a vehicleparameter 57159, a user experience parameter 57205, and combinationsthereof.

An aspect provided herein includes a recommendation system 57199 forrecommending a configuration of a vehicle 5710, the recommendationsystem 57199 comprising an expert system 5757 that produces arecommendation of a parameter for configuring a vehicle control system57134 that controls at least one of a vehicle parameter 57159 and avehicle rider experience parameter 57205.

In embodiments, the vehicle 5710 comprises a system for automating atleast one control parameter of the vehicle 5710. In embodiments, thevehicle is at least a semi-autonomous vehicle. In embodiments, thevehicle is automatically routed. In embodiments, the vehicle is aself-driving vehicle.

In embodiments, the expert system 5757 is a neural network system. Inembodiments, the expert system 5757 is a deep learning system. Inembodiments, the expert system 5757 is a machine learning system. Inembodiments, the expert system 5757 is a model-based system. Inembodiments, the expert system 5757 is a rule-based system. Inembodiments, the expert system 5757 is a random walk-based system. Inembodiments, the expert system 5757 is a genetic algorithm system. Inembodiments, the expert system 5757 is a convolutional neural networksystem. In embodiments, the expert system 5757 is a self-organizingsystem. In embodiments, the expert system 5757 is a pattern recognitionsystem. In embodiments, the expert system 5757 is a hybrid artificialintelligence-based system. In embodiments, the expert system 5757 is anacrylic graph-based system.

In embodiments, the expert system 5757 produces a recommendation basedon degrees of satisfaction of a plurality of riders of vehicles 5710 inthe transportation system 5711. In embodiments, the expert system 5757produces a recommendation based on a rider entertainment degree ofsatisfaction. In embodiments, the expert system 5757 produces arecommendation based on a rider safety degree of satisfaction. Inembodiments, the expert system 5757 produces a recommendation based on arider comfort degree of satisfaction. In embodiments, the expert system5757 produces a recommendation based on a rider in-vehicle search degreeof satisfaction.

In embodiments, the at least one rider (or user) experience parameter57205 is a parameter of traffic congestion. In embodiments, the at leastone rider experience parameter 57205 is a parameter of desired arrivaltimes. In embodiments, the at least one rider experience parameter 57205is a parameter of preferred routes. In embodiments, the at least onerider experience parameter 57205 is a parameter of fuel efficiency. Inembodiments, the at least one rider experience parameter 57205 is aparameter of pollution reduction. In embodiments, the at least one riderexperience parameter 57205 is a parameter of accident avoidance. Inembodiments, the at least one rider experience parameter 57205 is aparameter of avoiding bad weather. In embodiments, the at least onerider experience parameter 57205 is a parameter of avoiding bad roadconditions. In embodiments, the at least one rider experience parameter57205 is a parameter of reduced fuel consumption. In embodiments, the atleast one rider experience parameter 57205 is a parameter of reducedcarbon footprint. In embodiments, the at least one rider experienceparameter 57205 is a parameter of reduced noise in a region. Inembodiments, the at least one rider experience parameter 57205 is aparameter of avoiding high-crime regions.

In embodiments, the at least one rider experience parameter 57205 is aparameter of collective satisfaction. In embodiments, the at least onerider experience parameter 57205 is a parameter of maximum speed limit.In embodiments, the at least one rider experience parameter 57205 is aparameter of avoidance of toll roads. In embodiments, the at least onerider experience parameter 57205 is a parameter of avoidance of cityroads. In embodiments, the at least one rider experience parameter 57205is a parameter of avoidance of undivided highways. In embodiments, theat least one rider experience parameter 57205 is a parameter ofavoidance of left turns. In embodiments, the at least one riderexperience parameter 57205 is a parameter of avoidance ofdriver-operated vehicles.

In embodiments, the at least one vehicle parameter 57159 is a parameterof fuel consumption. In embodiments, the at least one vehicle parameter57159 is a parameter of carbon footprint. In embodiments, the at leastone vehicle parameter 57159 is a parameter of vehicle speed. Inembodiments, the at least one vehicle parameter 57159 is a parameter ofvehicle acceleration. In embodiments, the at least one vehicle parameter57159 is a parameter of travel time.

In embodiments, the expert system 5757 produces a recommendation basedon at least one of user behavior of the rider (e.g., user 5790) andrider interactions with content access interfaces 57206 of the vehicle5710. In embodiments, the expert system 5757 produces a recommendationbased on similarity of a profile of the rider (e.g., user 5790) toprofiles of other riders. In embodiments, the expert system 5757produces a recommendation based on a result of collaborative filteringdetermined through querying the rider (e.g., user 5790) and taking inputthat facilitates classifying rider responses thereto on a scale ofresponse classes ranging from favorable to unfavorable. In embodiments,the expert system 5757 produces a recommendation based on contentrelevant to the rider (e.g., user 5790) including at least one selectedfrom the group consisting of classification of trip, time of day,classification of road, trip duration, configured route, and number ofriders.

Referring now to FIG. 58 , an example transportation system 5811 isdepicted having a search system 58207 that is configured to providenetwork search results for in-vehicle searchers.

Self-driving vehicles offer their riders greatly increased opportunityto engage with in-vehicle interfaces, such as touch screens, virtualassistants, entertainment system interfaces, communication interfaces,navigation interfaces, and the like. While systems exist to display theinterface of a rider's mobile device on an in-vehicle interface, thecontent displayed on a mobile device screen is not necessarily tuned tothe unique situation of a rider in a vehicle. In fact, riders invehicles may be collectively quite different in their immediate needsfrom other individuals who engage with the interfaces, as the presencein the vehicle itself tends to indicate a number of things that aredifferent from a user sitting at home, sitting at a desk, or walkingaround. One activity that engages almost all device users is searching,which is undertaken on many types of devices (desktops, mobile devices,wearable devices, and others). Searches typically include keyword entry,which may include natural language text entry or spoken queries. Queriesare processed to provide search results, in one or more lists or menuelements, often involving delineation between sponsored results andnon-sponsored results. Ranking algorithms typically factor in a widerange of inputs, in particular the extent of utility (such as indicatedby engagement, clicking, attention, navigation, purchasing, viewing,listening, or the like) of a given search result to other users, suchthat more useful items are promoted higher in lists.

However, the usefulness of a search result may be very different for arider in a self-driving vehicle than for more general searchers. Forexample, a rider who is being driven on a defined route (as the route isa necessary input to the self-driving vehicle) may be far more likely tovalue search results that are relevant to locations that are ahead ofthe rider on the route than the same individual would be sitting at theindividual's desk at work or on a computer at home. Accordingly,conventional search engines may fail to deliver the most relevantresults, deliver results that crowd out more relevant results, and thelike, when considering the situation of a rider in a self-drivingvehicle.

In embodiments of the system 5811 of FIG. 58 , a search result rankingsystem (search system 58207) may be configured to providein-vehicle-relevant search results. In embodiments, such a configurationmay be accomplished by segmenting a search result ranking algorithm toinclude ranking parameters that are observed in connection only with aset of in-vehicle searches, so that in-vehicle results are ranked basedon outcomes with respect to in-vehicle searches by other users. Inembodiments, such a configuration may be accomplished by adjusting theweighting parameters applied to one or more weights in a conventionalsearch algorithm when an in-vehicle search is detected (such as bydetecting an indicator of an in-vehicle system, such as by communicationprotocol type, IP address, presence of cookies stored on a vehicle,detection of mobility, or the like). For example, local search resultsmay be weighted more heavily in a ranking algorithm.

In embodiments, routing information from a vehicle 5810 may be used asan input to a ranking algorithm, such as allowing favorable weighting ofresults that are relevant to local points of interest ahead on a route.

In embodiments, content types may be weighted more heavily in searchresults based on detection of an in-vehicle query, such as weatherinformation, traffic information, event information and the like. Inembodiments, outcomes tracked may be adjusted for in-vehicle searchrankings, such as by including route changes as a factor in rankings(e.g., where a search result appears to be associated in time with aroute change to a location that was the subject of a search result), byincluding rider feedback on search results (such as satisfactionindicators for a ride), by detecting in-vehicle behaviors that appear toderive from search results (such as playing music that appeared in asearch result), and the like.

In embodiments, a set of in-vehicle-relevant search results may beprovided in a separate portion of a search result interface (e.g., arider interface 58208), such as in a portion of a window that allows arider 57120 to see conventional search engine results, sponsored searchresults and in-vehicle relevant search results. In embodiments, bothgeneral search results and sponsored search results may be configuredusing any of the techniques described herein or other techniques thatwould be understood by skilled in the art to provide in-vehicle-relevantsearch results.

In embodiments where in-vehicle-relevant search results and conventionalsearch results are presented in the same interface (e.g., the riderinterface 58208), selection and engagement with in-vehicle-relevantsearch results can be used as a success metric to train or reinforce oneor more search algorithms 58211. In embodiments, in-vehicle searchalgorithms 58211 may be trained using machine learning, optionallyseeded by one or more conventional search models, which may optionallybe provided with adjusted initial parameters based on one or more modelsof user behavior that may contemplate differences between in-vehiclebehavior and other behavior. Machine learning may include use of neuralnetworks, deep learning systems, model-based systems, and others.Feedback to machine learning may include conventional engagement metricsused for search, as well as metrics of rider satisfaction, emotionalstate, yield metrics (e.g., for sponsored search results, banner ads,and the like), and the like.

An aspect provided herein includes a system for transportation 5811,comprising: a search system 58207 to provide network search results forin-vehicle searchers.

An aspect provided herein includes an in-vehicle network search system58207 of a vehicle 5810, the search system comprising: a rider interface58208 through which the rider 58120 of the vehicle 5810 is enabled toengage with the search system 58207; a search result generating circuit58209 that favors search results based on a set of in-vehicle searchcriteria that are derived from a plurality of in-vehicle searchespreviously conducted; and a search result display ranking circuit 58210that orders the favored search results based on a relevance of alocation component of the search results with a configured route of thevehicle 5810.

In embodiments, the vehicle 5810 comprises a system for automating atleast one control parameter of the vehicle 5810. In embodiments, thevehicle 5810 is at least a semi-autonomous vehicle. In embodiments, thevehicle 5810 is automatically routed. In embodiments, the vehicle 5810is a self-driving vehicle.

In embodiments, the rider interface 58208 comprises at least one of atouch screen, a virtual assistant, an entertainment system interface, acommunication interface and a navigation interface.

In embodiments, the favored search results are ordered by the searchresult display ranking circuit 58210 so that results that are proximalto the configured route appear before other results. In embodiments, thein-vehicle search criteria are based on ranking parameters of a set ofin-vehicle searches. In embodiments, the ranking parameters are observedin connection only with the set of in-vehicle searches. In embodiments,the search system 58207 adapts the search result generating circuit58209 to favor search results that correlate to in-vehicle behaviors. Inembodiments, the search results that correlate to in-vehicle behaviorsare determined through comparison of rider behavior before and afterconducting a search. In embodiments, the search system further comprisesa machine learning circuit 58212 that facilitates training the searchresult generating circuit 58209 from a set of search results for aplurality of searchers and a set of search result generating parametersbased on an in-vehicle rider behavior model.

An aspect provided herein includes an in-vehicle network search system58207 of a vehicle 5810, the search system 58207 comprising: a riderinterface 58208 through which the rider 58120 of the vehicle 5810 isenabled to engage with the search system 5810; a search resultgenerating circuit 58209 that varies search results based on detectionof whether the vehicle 5810 is in self-driving or autonomous mode orbeing driven by an active driver; and a search result display rankingcircuit 58210 that orders the search results based on a relevance of alocation component of the search results with a configured route of thevehicle 5810. In embodiments, the search results vary based on whetherthe user (e.g., the rider 58120) is a driver of the vehicle 5810 or apassenger in the vehicle 5810.

In embodiments, the vehicle 5810 comprises a system for automating atleast one control parameter of the vehicle 5810. In embodiments, thevehicle 5810 is at least a semi-autonomous vehicle. In embodiments, thevehicle 5810 is automatically routed. In embodiments, the vehicle 5810is a self-driving vehicle.

In embodiments, the rider interface 58208 comprises at least one of atouch screen, a virtual assistant, an entertainment system interface, acommunication interface and a navigation interface.

In embodiments, the search results are ordered by the search resultdisplay ranking circuit 58210 so that results that are proximal to theconfigured route appear before other results.

In embodiments, search criteria used by the search result generatingcircuit 58209 are based on ranking parameters of a set of in-vehiclesearches. In embodiments, the ranking parameters are observed inconnection only with the set of in-vehicle searches. In embodiments, thesearch system 58207 adapts the search result generating circuit 58209 tofavor search results that correlate to in-vehicle behaviors. Inembodiments, the search results that correlate to in-vehicle behaviorsare determined through comparison of rider behavior before and afterconducting a search. In embodiments, the search system 58207 furthercomprises a machine learning circuit 58212 that facilitates training thesearch result generating circuit 58209 from a set of search results fora plurality of searchers and a set of search result generatingparameters based on an in-vehicle rider behavior model.

An aspect provided herein includes an in-vehicle network search system58207 of a vehicle 5810, the search system 58207 comprising: a riderinterface 58208 through which the rider 58120 of the vehicle 5810 isenabled to engage with the search system 58207; a search resultgenerating circuit 58209 that varies search results based on whether theuser (e.g., the rider 58120) is a driver of the vehicle or a passengerin the vehicle; and a search result display ranking circuit 58210 thatorders the search results based on a relevance of a location componentof the search results with a configured route of the vehicle 5810.

In embodiments, the vehicle 5810 comprises a system for automating atleast one control parameter of the vehicle 5810. In embodiments, thevehicle 5810 is at least a semi-autonomous vehicle. In embodiments, thevehicle 5810 is automatically routed. In embodiments, the vehicle 5810is a self-driving vehicle.

In embodiments, the rider interface 58208 comprises at least one of atouch screen, a virtual assistant, an entertainment system interface, acommunication interface and a navigation interface.

In embodiments, the search results are ordered by the search resultdisplay ranking circuit 58210 so that results that are proximal to theconfigured route appear before other results. In embodiments, searchcriteria used by the search result generating circuit 58209 are based onranking parameters of a set of in-vehicle searches. In embodiments, theranking parameters are observed in connection only with the set ofin-vehicle searches.

In embodiments, the search system 58204 adapts the search resultgenerating circuit 58209 to favor search results that correlate toin-vehicle behaviors. In embodiments, the search results that correlateto in-vehicle behaviors are determined through comparison of riderbehavior before and after conducting a search. In embodiments, thesearch system 58207, further comprises a machine learning circuit 58212that facilitates training the search result generating circuit 58209from a set of search results for a plurality of searchers and a set ofsearch result generating parameters based on an in-vehicle riderbehavior model.

Having thus described several aspects and embodiments of the technologyof this application, it is to be appreciated that various alterations,modifications, and improvements will readily occur to those skilled inthe art. Such alterations, modifications, and improvements are intendedto be within the spirit and scope of the technology described in theapplication. For example, those skilled in the art will readily envisiona variety of other means and/or structures for performing the functionand/or obtaining the results and/or one or more of the advantagesdescribed herein, and each of such variations and/or modifications isdeemed to be within the scope of the embodiments described herein.

Those skilled in the art will recognize or be able to ascertain using nomore than routine experimentation, many equivalents to the specificembodiments described herein. It is, therefore, to be understood thatthe foregoing embodiments are presented by way of example only and that,within the scope of the appended claims and equivalents thereto,inventive embodiments may be practiced otherwise than as specificallydescribed. In addition, any combination of two or more features,systems, articles, materials, kits, and/or methods described herein, ifsuch features, systems, articles, materials, kits, and/or methods arenot mutually inconsistent, is included within the scope of the presentdisclosure.

The above-described embodiments may be implemented in any of numerousways. One or more aspects and embodiments of the present applicationinvolving the performance of processes or methods may utilize programinstructions executable by a device (e.g., a computer, a processor, orother devices) to perform, or control performance of, the processes ormethods.

As used herein, the term system may define any combination of one ormore computing devices, processors, modules, software, firmware, orcircuits that operate either independently or in a distributed manner toperform one or more functions. A system may include one or moresubsystems.

In this respect, various inventive concepts may be embodied as acomputer readable storage medium (or multiple computer readable storagemedia) (e.g., a computer memory, one or more floppy discs, compactdiscs, optical discs, magnetic tapes, flash memories, circuitconfigurations in Field Programmable Gate Arrays or other semiconductordevices, or other tangible computer storage medium) encoded with one ormore programs that, when executed on one or more computers or otherprocessors, perform methods that implement one or more of the variousembodiments described above.

The computer readable medium or media may be transportable, such thatthe program or programs stored thereon may be loaded onto one or moredifferent computers or other processors to implement various ones of theaspects described above. In some embodiments, computer readable mediamay be non-transitory media.

The terms “program” or “software” are used herein in a generic sense torefer to any type of computer code or set of computer-executableinstructions that may be employed to program a computer or otherprocessor to implement various aspects as described above. Additionally,it should be appreciated that according to one aspect, one or morecomputer programs that when executed perform methods of the presentapplication need not reside on a single computer or processor, but maybe distributed in a modular fashion among a number of differentcomputers or processors to implement various aspects of the presentapplication.

Computer-executable instructions may be in many forms, such as programmodules, executed by one or more computers or other devices. Generally,program modules include routines, programs, objects, components, datastructures, etc. that performs particular tasks or implement particularabstract data types. Typically, the functionality of the program modulesmay be combined or distributed as desired in various embodiments.

Also, data structures may be stored in computer-readable media in anysuitable form. For simplicity of illustration, data structures may beshown to have fields that are related through location in the datastructure. Such relationships may likewise be achieved by assigningstorage for the fields with locations in a computer-readable medium thatconvey relationship between the fields. However, any suitable mechanismmay be used to establish a relationship between information in fields ofa data structure, including through the use of pointers, tags or othermechanisms that establish relationship between data elements.

Also, as described, some aspects may be embodied as one or more methods.The acts performed as part of the method may be ordered in any suitableway. Accordingly, embodiments may be constructed in which acts areperformed in an order different than illustrated, which may includeperforming some acts simultaneously, even though shown as sequentialacts in illustrative embodiments.

The present disclosure should therefore not be considered limited to theparticular embodiments described above. Various modifications,equivalent processes, as well as numerous structures to which thepresent disclosure may be applicable, will be readily apparent to thoseskilled in the art to which the present disclosure is directed uponreview of the present disclosure.

Detailed embodiments of the present disclosure are disclosed herein;however, it is to be understood that the disclosed embodiments aremerely exemplary of the disclosure, which may be embodied in variousforms. Therefore, specific structural and functional details disclosedherein are not to be interpreted as limiting, but merely as a basis forthe claims and as a representative basis for teaching one skilled in theart to variously employ the present disclosure in virtually anyappropriately detailed structure.

The terms “a” or “an,” as used herein, are defined as one or more thanone. The term “another,” as used herein, is defined as at least a secondor more. The terms “including” and/or “having,” as used herein, aredefined as comprising (i.e., open transition).

While only a few embodiments of the present disclosure have been shownand described, it will be obvious to those skilled in the art that manychanges and modifications may be made thereunto without departing fromthe spirit and scope of the present disclosure as described in thefollowing claims. All patent applications and patents, both foreign anddomestic, and all other publications referenced herein are incorporatedherein in their entireties to the full extent permitted by law.

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software, program codes,and/or instructions on a processor. The present disclosure may beimplemented as a method on the machine, as a system or apparatus as partof or in relation to the machine, or as a computer program productembodied in a computer readable medium executing on one or more of themachines. In embodiments, the processor may be part of a server, cloudserver, client, network infrastructure, mobile computing platform,stationary computing platform, or other computing platform. A processormay be any kind of computational or processing device capable ofexecuting program instructions, codes, binary instructions and the like.The processor may be or may include a signal processor, digitalprocessor, embedded processor, microprocessor or any variant such as aco-processor (math co-processor, graphic co-processor, communicationco-processor and the like) and the like that may directly or indirectlyfacilitate execution of program code or program instructions storedthereon. In addition, the processor may enable execution of multipleprograms, threads, and codes. The threads may be executed simultaneouslyto enhance the performance of the processor and to facilitatesimultaneous operations of the application. By way of implementation,methods, program codes, program instructions and the like describedherein may be implemented in one or more thread. The thread may spawnother threads that may have assigned priorities associated with them;the processor may execute these threads based on priority or any otherorder based on instructions provided in the program code. The processor,or any machine utilizing one, may include non-transitory memory thatstores methods, codes, instructions and programs as described herein andelsewhere. The processor may access a non-transitory storage mediumthrough an interface that may store methods, codes, and instructions asdescribed herein and elsewhere. The storage medium associated with theprocessor for storing methods, programs, codes, program instructions orother type of instructions capable of being executed by the computing orprocessing device may include but may not be limited to one or more of aCD-ROM, DVD, memory, hard disk, flash drive, RAM, ROM, cache and thelike.

A processor may include one or more cores that may enhance speed andperformance of a multiprocessor. In embodiments, the process may be adual core processor, quad core processors, other chip-levelmultiprocessor and the like that combine two or more independent cores(called a die).

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software on a server,client, firewall, gateway, hub, router, or other such computer and/ornetworking hardware. The software program may be associated with aserver that may include a file server, print server, domain server,internet server, intranet server, cloud server, and other variants suchas secondary server, host server, distributed server and the like. Theserver may include one or more of memories, processors, computerreadable media, storage media, ports (physical and virtual),communication devices, and interfaces capable of accessing otherservers, clients, machines, and devices through a wired or a wirelessmedium, and the like. The methods, programs, or codes as describedherein and elsewhere may be executed by the server. In addition, otherdevices required for execution of methods as described in thisapplication may be considered as a part of the infrastructure associatedwith the server.

The server may provide an interface to other devices including, withoutlimitation, clients, other servers, printers, database servers, printservers, file servers, communication servers, distributed servers,social networks, and the like. Additionally, this coupling and/orconnection may facilitate remote execution of program across thenetwork. The networking of some or all of these devices may facilitateparallel processing of a program or method at one or more locationwithout deviating from the scope of the disclosure. In addition, any ofthe devices attached to the server through an interface may include atleast one storage medium capable of storing methods, programs, codeand/or instructions. A central repository may provide programinstructions to be executed on different devices. In thisimplementation, the remote repository may act as a storage medium forprogram code, instructions, and programs.

The software program may be associated with a client that may include afile client, print client, domain client, internet client, intranetclient and other variants such as secondary client, host client,distributed client and the like. The client may include one or more ofmemories, processors, computer readable media, storage media, ports(physical and virtual), communication devices, and interfaces capable ofaccessing other clients, servers, machines, and devices through a wiredor a wireless medium, and the like. The methods, programs, or codes asdescribed herein and elsewhere may be executed by the client. Inaddition, other devices required for execution of methods as describedin this application may be considered as a part of the infrastructureassociated with the client.

The client may provide an interface to other devices including, withoutlimitation, servers, other clients, printers, database servers, printservers, file servers, communication servers, distributed servers andthe like. Additionally, this coupling and/or connection may facilitateremote execution of program across the network. The networking of someor all of these devices may facilitate parallel processing of a programor method at one or more location without deviating from the scope ofthe disclosure. In addition, any of the devices attached to the clientthrough an interface may include at least one storage medium capable ofstoring methods, programs, applications, code and/or instructions. Acentral repository may provide program instructions to be executed ondifferent devices. In this implementation, the remote repository may actas a storage medium for program code, instructions, and programs.

The methods and systems described herein may be deployed in part or inwhole through network infrastructures. The network infrastructure mayinclude elements such as computing devices, servers, routers, hubs,firewalls, clients, personal computers, communication devices, routingdevices and other active and passive devices, modules and/or componentsas known in the art. The computing and/or non-computing device(s)associated with the network infrastructure may include, apart from othercomponents, a storage medium such as flash memory, buffer, stack, RAM,ROM and the like. The processes, methods, program codes, instructionsdescribed herein and elsewhere may be executed by one or more of thenetwork infrastructural elements. The methods and systems describedherein may be adapted for use with any kind of private, community, orhybrid cloud computing network or cloud computing environment, includingthose which involve features of software as a service (SaaS), platformas a service (PaaS), and/or infrastructure as a service (IaaS).

The methods, program codes, and instructions described herein andelsewhere may be implemented on or through mobile devices. The mobiledevices may include navigation devices, cell phones, mobile phones,mobile personal digital assistants, laptops, palmtops, netbooks, pagers,electronic books readers, music players and the like. These devices mayinclude, apart from other components, a storage medium such as a flashmemory, buffer, RAM, ROM and one or more computing devices. Thecomputing devices associated with mobile devices may be enabled toexecute program codes, methods, and instructions stored thereon.Alternatively, the mobile devices may be configured to executeinstructions in collaboration with other devices. The mobile devices maycommunicate with base stations interfaced with servers and configured toexecute program codes. The mobile devices may communicate on apeer-to-peer network, mesh network, or other communications network. Theprogram code may be stored on the storage medium associated with theserver and executed by a computing device embedded within the server.The base station may include a computing device and a storage medium.The storage device may store program codes and instructions executed bythe computing devices associated with the base station.

The computer software, program codes, and/or instructions may be storedand/or accessed on machine readable media that may include: computercomponents, devices, and recording media that retain digital data usedfor computing for some interval of time; semiconductor storage known asrandom access memory (RAM); mass storage typically for more permanentstorage, such as optical discs, forms of magnetic storage like harddisks, tapes, drums, cards and other types; processor registers, cachememory, volatile memory, non-volatile memory; optical storage such asCD, DVD; removable media such as flash memory (e.g., USB sticks orkeys), floppy disks, magnetic tape, paper tape, punch cards, standaloneRAM disks, Zip drives, removable mass storage, off-line, and the like;other computer memory such as dynamic memory, static memory, read/writestorage, mutable storage, read only, random access, sequential access,location addressable, file addressable, content addressable, networkattached storage, storage area network, bar codes, magnetic ink, and thelike.

The methods and systems described herein may transform physical and/orintangible items from one state to another. The methods and systemsdescribed herein may also transform data representing physical and/orintangible items from one state to another.

The elements described and depicted herein, including in flowcharts andblock diagrams throughout the figures, imply logical boundaries betweenthe elements. However, according to software or hardware engineeringpractices, the depicted elements and the functions thereof may beimplemented on machines through computer executable media having aprocessor capable of executing program instructions stored thereon as amonolithic software structure, as standalone software modules, or asmodules that employ external routines, code, services, and so forth, orany combination of these, and all such implementations may be within thescope of the present disclosure. Examples of such machines may include,but may not be limited to, personal digital assistants, laptops,personal computers, mobile phones, other handheld computing devices,medical equipment, wired or wireless communication devices, transducers,chips, calculators, satellites, tablet PCs, electronic books, gadgets,electronic devices, devices having artificial intelligence, computingdevices, networking equipment, servers, routers and the like.Furthermore, the elements depicted in the flowchart and block diagramsor any other logical component may be implemented on a machine capableof executing program instructions. Thus, while the foregoing drawingsand descriptions set forth functional aspects of the disclosed systems,no particular arrangement of software for implementing these functionalaspects should be inferred from these descriptions unless explicitlystated or otherwise clear from the context. Similarly, it will beappreciated that the various steps identified and described above may bevaried, and that the order of steps may be adapted to particularapplications of the techniques disclosed herein. All such variations andmodifications are intended to fall within the scope of this disclosure.As such, the depiction and/or description of an order for various stepsshould not be understood to require a particular order of execution forthose steps, unless required by a particular application, or explicitlystated or otherwise clear from the context.

The methods and/or processes described above, and steps associatedtherewith, may be realized in hardware, software or any combination ofhardware and software suitable for a particular application. Thehardware may include a general-purpose computer and/or dedicatedcomputing device or specific computing device or particular aspect orcomponent of a specific computing device. The processes may be realizedin one or more microprocessors, microcontrollers, embeddedmicrocontrollers, programmable digital signal processors or otherprogrammable device, along with internal and/or external memory. Theprocesses may also, or instead, be embodied in an application specificintegrated circuit, a programmable gate array, programmable array logic,or any other device or combination of devices that may be configured toprocess electronic signals. It will further be appreciated that one ormore of the processes may be realized as a computer executable codecapable of being executed on a machine-readable medium.

The computer executable code may be created using a structuredprogramming language such as C, an object oriented programming languagesuch as C++, or any other high-level or low-level programming language(including assembly languages, hardware description languages, anddatabase programming languages and technologies) that may be stored,compiled or interpreted to run on one of the above devices, as well asheterogeneous combinations of processors, processor architectures, orcombinations of different hardware and software, or any other machinecapable of executing program instructions.

Thus, in one aspect, methods described above and combinations thereofmay be embodied in computer executable code that, when executing on oneor more computing devices, performs the steps thereof. In anotheraspect, the methods may be embodied in systems that perform the stepsthereof, and may be distributed across devices in a number of ways, orall of the functionality may be integrated into a dedicated, standalonedevice or other hardware. In another aspect, the means for performingthe steps associated with the processes described above may include anyof the hardware and/or software described above. All such permutationsand combinations are intended to fall within the scope of the presentdisclosure.

While the disclosure has been disclosed in connection with the preferredembodiments shown and described in detail, various modifications andimprovements thereon will become readily apparent to those skilled inthe art. Accordingly, the spirit and scope of the present disclosure isnot to be limited by the foregoing examples but is to be understood inthe broadest sense allowable by law.

The use of the terms “a” and “an” and “the” and similar referents in thecontext of describing the disclosure (especially in the context of thefollowing claims) is to be construed to cover both the singular and theplural, unless otherwise indicated herein or clearly contradicted bycontext. The term “set” should be understood to include a set of asingle member or multiple members. The terms “comprising,” “having,”“including,” and “containing” are to be construed as open-ended terms(i.e., meaning “including, but not limited to,”) unless otherwise noted.Recitations of ranges of values herein are merely intended to serve as ashorthand method of referring individually to each separate valuefalling within the range, unless otherwise indicated herein, and eachseparate value is incorporated into the specification as if it wereindividually recited herein. All methods described herein can beperformed in any suitable order unless otherwise indicated herein orotherwise clearly contradicted by context. The use of any and allexamples, or exemplary language (e.g., “such as”) provided herein, isintended merely to better illuminate the disclosure and does not pose alimitation on the scope of the disclosure unless otherwise claimed. Nolanguage in the specification should be construed as indicating anynon-claimed element as essential to the practice of the disclosure.

While the foregoing written description enables one skilled to make anduse what is considered presently to be the best mode thereof, thoseskilled in the art will understand and appreciate the existence ofvariations, combinations, and equivalents of the specific embodiment,method, and examples herein. The disclosure should therefore not belimited by the above described embodiment, method, and examples, but byall embodiments and methods within the scope and spirit of thedisclosure.

Any element in a claim that does not explicitly state “means for”performing a specified function, or “step for” performing a specifiedfunction, is not to be interpreted as a “means” or “step” clause asspecified in 35 U.S.C. § 112(f). In particular, any use of “step of” inthe claims is not intended to invoke the provision of 35 U.S.C. §112(f).

Those skilled in the art may appreciate that numerous designconfigurations may be possible to enjoy the functional benefits of theinventive systems. Thus, given the wide variety of configurations andarrangements of embodiments of the present disclosure, the scope of thedisclosure is reflected by the breadth of the claims below rather thannarrowed by the embodiments described above.

What is claimed is:
 1. A data processing system that promotessatisfaction of a rider of a vehicle, the data processing systemcomprising: a machine learning model that determines a measure of anemotional state of the rider based on data received from a sensorassociated with the rider, wherein the data is indicative of aphysiological condition of the rider; and a vehicle control system that:determines a target value of an operating parameter of the vehicle basedon a correlation between the emotional state of the rider and the targetvalue of the operating parameter; and adjusts the operating parameter ofthe vehicle based on the target value of the operating parameter.
 2. Thedata processing system of claim 1, further comprising a wearable sensorconfigured to be worn by the rider, to collect data indicative of aphysiological condition of the rider, and to communicate the dataindicative of a physiological condition to the vehicle control system.3. The data processing system of claim 1, wherein the machine learningmodel includes a first neural network.
 4. The data processing system ofclaim 3, wherein the first neural network determines the measure of theemotional state.
 5. The data processing system of claim 4, wherein thevehicle control system includes a second neural network that adjusts theoperating parameter of the vehicle.
 6. The data processing system ofclaim 5, wherein the second neural network optimizes the operationalparameter in real time responsive to the measure of the emotional stateof the rider determined by the first neural network.
 7. The dataprocessing system of claim 5, wherein the first neural network comprisesa plurality of connected nodes that form a directed cycle, the firstneural network further facilitating bi-directional flow of data amongthe plurality of connected nodes.
 8. The data processing system of claim5, wherein the first neural network is a recurrent neural network toindicate a change in an emotional state of a rider in a vehicle throughrecognition of patterns of physiological data of the rider captured bythe sensor associated with the rider.
 9. The data processing system ofclaim 8, wherein the second neural network is a radial basis functionneural network to optimize, for achieving a favorable emotional state ofthe rider, an operational parameter of the vehicle in response to theindication of change in the emotional state of the rider.
 10. The dataprocessing system of claim 9, wherein the radial basis function neuralnetwork determines the operating parameter of the vehicle that is to beadjusted.
 11. The data processing system of claim 1, wherein the vehiclecontrol system determines the operating parameter of the vehicle that isto be adjusted.